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Chapter 6: The open science commons (plural), networks, and collaboration

Published onDec 22, 2023
Chapter 6: The open science commons (plural), networks, and collaboration

Commons are the future homes for science

Open science builds scholarly commons (plural) across the planet

Open science movements agree that the future depends on commons, even when the word is not present. NOTE: the photos in this section are of the California coast, which is in its entire length a public commons.

“We see a future in which scientific information and scholarly communication more generally become part of a global, universal and explicit network of knowledge; where every claim, hypothesis, argument—every significant element of the discourse—can be explicitly represented, along with supporting data, software, workflows, multimedia, external commentary, and information about provenance. In this world of networked knowledge objects, it would be clear how the entities and discourse components are related to each other, including relationships to previous scholarship; learning about a new topic means absorbing networks of information, not individually reading thousands of documents. Adding new elements of scholarly knowledge is achieved by adding nodes and relationships to this network. People could contribute to the network from a variety of perspectives; each contribution would be immediately accessible globally by others. Reviewing procedures, as well as reputation management mechanisms, would provide ways to evaluate and filter information.”-FORCE11 Manifesto

“For the first time ever, the Internet now offers the chance to constitute a global and interactive representation of human knowledge, including cultural heritage and the guarantee of worldwide access”(Preface to the Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities, 2003; Accessed April 13, 2020).

“Scholarly communication should expand the knowledge commons. Scientific knowledge is critical for the development of society. As scientific knowledge is intangible in nature, its use by one person does not preclude its use by another person. On the contrary, knowledge tends to grow when it is shared. Therefore, no barriers should be established to restrict the use and reuse of research results. Scientific knowledge should be a public good and as such part of the knowledge commons, in order to enable everyone in society to benefit from this knowledge.”-Principle #12, Innovation, Vienna Principles

Starting points toward commoning in open science

In the following, “commons,” “academy commons,” “scholarly commons,” and “science commons” refer to any commons created to house academic shared-pool resources—including networks set up for online conversations—and governed by a community that uses these for their research.

Many thanks to the Alfred P. Sloan Foundation for supporting this work on the scholarly commons.

  • Science is intensely personal. Scientists are already engaged in their own struggle with the unknowns of nature through infinite play. Science—their intellectual disease—is fortunately incurable, and likely pandemic.

  • Science is already social. Just in the US, several thousand workshops a year evidence the scientific need/desire to build collective knowledge.

  • Science is cultural. Self-governed science communities can use intentional cultural practices to help scientists prepare to work together in virtual organizations with shared norms and resources.

  • Commoning communities open up arenas for online collaboration. Online conversation-driven collectives supported by virtual communities on internet platforms can replace expensive in-person workshops and massive annual meetings, and enable scientists to share knowledge and solve problems today across the globe.

  • These communities need to consider themselves as commons to replace institutions that have been twisted by the three dimensions of external goods and influence (hierarchy, intellectual property, and neoliberal economics). Commons can address the many intellectual property wrongs that plague the academy today.

  • Each commons needs to work locally, attuned to its local situation within science domains and academic institutions.

  • The academy needs to harness the internet and technology platforms to knit together localized science/data commons into a global web of open shared resources and collective intelligence.

Scholarly commons are…

Intentional communities (plural) formed around the shared use of open scholarly resources (a type of common-pool resource). Commoners work together as a community to optimize the use of the open resources they share. Scholarly commons are resource-near communities. They have an immediate and professional stake in the open resources they need to use for their research. The whole community assumes a stewardship role toward these resources. These groups are self-defining and self-governing, each with their own emergent rules. 

Since scholarly commons are usually built upon open public resources, anybody on the planet can access them. When these are digital resources, they are not diminished by overuse. However, these resources cannot be sustained without the commons, or some other economy. These commons represent the social/cultural destination for any number of open-science efforts.

Scholarly commoners are…

Members of these intentional communities, with the freedoms and responsibilities that their communities provide and demand. Commoners work for the benefit of the whole community and for the sustainability of its open, shared scholarly resources. An individual commoner may belong to several commons. It is the role and the goal of commoners to help these open, shared resources flourish.

Membership is implicit in a commons, and represents an active agreement to respect and celebrate the shared principles of the group. Membership will also require some attention commitment to governance and service.

Scholarly commoning is…

The practice (and an attitude) that commoners bring to the scholarly commons. It begins with a logic of abundance, and depends on an active culture of sharing. Commoning is the activity to build and sustain the commons through shared practice (thanks to Cameron Neylon for this wording). Scholarly commoning is also imbued with an ethos of scholarship/science (however defined). Scholarly commoning informs how science can be accomplished through the use of open, shared resources (open ideas, open data, open software, open workflows, open-access publishing with open review, etc.) inside commons, instead of through other types of economies.

Let’s now explore in some depth what commons look like and how they work toward “science done right.”

Commons start with people: a community of commoners.

To paraphrase Peter Linebaugh: “there is no commons without commoners.”

Commoners contribute to and help govern their commons in many ways. They contribute a wide range of research-related objects and data; they ensure that these are sharable and discoverable through the use of appropriate metadata; they create “cerebration” events (See: Knowing and Conversation) to share ideas and scholarly objects, they collaborate in the development and use of appropriate standards and stewardship efforts; they acknowledge the efforts of others in their work; they promote the commons and commoning as a mode of scholarly effort.

Because commons are owned and led by their communities, volunteers are given the responsibility to envision, build, and govern these as destinations for the future of open science and scholarship. All commoners will benefit from the impacts that their commons will make on the academy’s research and communication capabilities. Volunteer leaders will also gain satisfaction that their time and efforts will grow these resources for the benefit of all and the advancement of knowledge.

“Before every great idea is a crazy idea.”
Jono Bacon (2009)
“The world’s cognitive surplus is so large that small changes can have huge ramifications in aggregate.”
Clay Shirky (2010)

These commons are open to all participants who accept their principles

Commons can support a diversity of skills and knowledge without privileging any. All commoners will find a home for their knowledge and their interests. As a norm, participation in any scholarly commons is not restricted on the basis of accreditation, professional standing or reputation, or any other criteria except willingness to contribute and uphold the principles of the commons. Content and behavior are the only criteria for moderation within a commons.

Commons are intellectual “rooms” (See: Science happens elsewhere) that value active sharing and collaboration. Commoners serve these requirements in different ways across the spectrum of occupations and career paths. A commons does not require a specific volume or genre of contribution, a particular professional, educational, or social background, affiliation, certification, or status.

The reach of the global commons network is not restricted to participants from any single sector or region. This network provides a home for the work of full professors, citizen-scientists, entrepreneurs, and bloggers. It recognizes the comment, the scholarly monograph, the dataset, the discussion, and the commercial product or service. It provides a home and recognition for programmers, statisticians, bench scientists, and literary critics. It welcomes the most narrowly focused specialist work and the broadest popularization. Above all, it encourages commoners to collaborate and share their specializations and interests.

Each commoner gets more value than they give as they grow their scholarly commons (note: any scholar may belong to more than one of these). The return on investment (ROI) for the commoner demonstrates how a commons as a whole is more valuable than any of its pieces. One part of this equation is due to the “power of pull”, which amplifies the value of participation, and also the utility of each object being shared in the network.

Commons are self-identified by interests, disciplines, experiences, data sources and uses, and research goals. Commoners across the planet will be linked as their local commons builds networks with other commons to expand the “room” they share to animate their conversations and creativity.

“Definitions belong to the definers, not the defined” Toni Morrison.

Science commons welcome and encourage participants of all backgrounds

Fierce equality means that every commons welcomes and encourages participants of all genders, social, regional, ethnic, linguistic, and disciplinary backgrounds. It also recognizes that disagreement is an inherent part of research communication, including disagreement as to fundamental principles and theories.

A commons is an ecosystem that is defined by the interactions of each and every commoner who participates in jointly building and governing it. Just like every other ecosystem, a commons cannot be a monoculture; instead, it needs diversity in order to survive and thrive. While many scholarly disciplines differ in their culture of how to generate, treat and store their scholarly objects, their commons must be open to all of them.

In a similar way, scholarly commons can not only rely on the expertise of scholars employed at top-tier research universities. Instead, they need to be open and accessible to commoners that don’t fit the academic stereotypes, or indeed never were in academia. Creating scholarly objects and performing scholarly activities is not limited to the core academic scholarly community. This means that commons must be open to diverse research questions and answers, including those proposed by non-professionals.

Through its self-governance, a commons uses vigilance with regard to hidden and structural biases and impediments and humility and open-mindedness with regard to the life-experiences of others. Because a commons is a shared agreement, the onus for ensuring equality and diversity of access is on the commoners themselves.

As commoners build self-governance, they should consider statements on inclusivity and language policy, because these encourage critical reflection on structural impediments. Exclusion of participation based primarily on formal degrees and academic rank is discouraged. When such criteria are used, alternative routes to participation should be provided.

Fierce Equality among objects for the pooled resource collection

Commons accept all contributed objects that adhere to their guidelines on an equal basis regardless of form

In order to improve the breadth and pace of knowledge generation, a commons will accept any contributed object that adheres to its guidelines. Because commons are grounded by a logic of abundance and a goal of reuse, they do not serve as gatekeepers or pretend to know the ultimate knowledge-value of any of their shared objects.

This means that there is no test of value, impact, significance, relevance, or endorsement that can be used to determine what belongs within a commons. Blog postings are as eligible as scholarly monographs. Highly cited papers are as welcome as preprints. Ground-breaking studies are as welcome as replication studies.

Once an object is in a commons, it is available for additional services. For example, commons services could be implemented to help commoners search for objects. Early versions of objects can be peer-reviewed. Objects can earn citations. Objects may be further curated or aggregated into collections by other commoners based on their expertise. Data objects can be evaluated for provenance and various qualities that improve their use and reuse.

Some services will not be provided inside a commons. For example, others in the academy may want to add metrics or rankings to objects in a commons. Commons have no objection to these services, however all forms of metrics should be built on transparent and open standards so that they may be reproduced and understood. Rankings will be made external to a commons and will not be housed inside the commons.

John Wilbanks: “Going back to the beginning of science: it used to belong to all of us.”

Smaldino and McElreath (2016): “when a measure becomes a target, it ceases to be a good measure.

“We reaffirm the principle that only the intrinsic merit of the work, and not the title of the journal in which a candidate’s work is published, will be considered in appointments, promotions, merit awards or grants.”-Bethesda Statement on Open Access Publishing, 2003; Accessed April 9, 2020.

“Do not use journal-based metrics, such as Journal Impact Factors, as a surrogate measure of the quality of individual research articles, to assess an individual scientist’s contributions, or in hiring, promotion, or funding decisions.”-DORA; Accessed April 9, 2020.

Science commons have no intrinsic hierarchies, rankings, or reward systems

All participants and all research objects that conform to the principles of the commons are equally appropriate and available for dissemination and reuse. Attribution systems and formats are driven by the demands of transparency and the intrinsic nature of research, rather than the requirements of any reward system. Intellectual humility (See: Kindness, Culture, and Caring) is expected in these commons as internal good and a norm for science, crowding out bullshit prestige.

All contributors are acknowledged on an equal basis (meaning there is no intrinsic difference between authorial and other acknowledgements); all forms of dissemination are accepted on an equal basis (meaning there is no hierarchy among genres or formats). Commoners are expected to match the form of dissemination to the needs of the research output rather than the demands of a reward system. None of this is compatible with systems that create hierarchies among types or forms of contribution or encourage dissemination in one format over another.

The fundamental premises of a science commons are incompatible with “scooping”, because the commons does not require these ideas to be new or unique as a condition of entry, even though the commons tracks when and where ideas and objects enter the commons.

“...[I]f we could solve the problem of open access within the university—that is to say, prove that the economic equation of doing research, reviewing it, and making it freely available for everyone works, then we could prove that the tyranny of the margin need not operate everywhere.” (Kelty 2014)

“Our mission of disseminating knowledge is only half complete if the information is not made widely and readily available to society.”-Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities; Accessed April 9, 2020.

“Computer analysis of content in all formats, that is content mining, enables access to undiscovered public knowledge and provides important insights across every aspect of our economic, social and cultural life.”-Hague Declaration on Knowledge Discovery in the Digital Age; Accessed April 9, 2020.

“When intellectual property law allows content to be read and analysed manually by humans but not by their machines, it has failed its original purposes.-Hague Declaration on Knowledge Discovery in the Digital Age; Accessed April 9, 2020.

A commons is open by default, with a culture of demand sharing

Openness for demand sharing is the core norm for a commons. Its resources are intentionally and reflexively open and entirely free to use, read, reuse, and remix by humans and machines, unless there is a compelling reason to restrict access, e.g., personal health information. Scholarly commoning starts with openness as a norm, and supports activities that explore open scholarship fully. Demand sharing is the main activity in a commons.

Commons can use standards and guidelines developed by other organizations (e.g., OKFN Open Definition, Budapest Open Access Initiative guidelines, and the Open Source Initiative definition) to inform their core definition of open content and access. Openness will be reinforced through the use of licenses that support the sharing of outcomes, such as knowledge gained by mining commons resources, research undertaken using commons resources, and software derived from commons code.

Commons will support a variety of open licenses. In their daily practice, commoners heed the requirements of these licenses and add their own content through them. Open includes promoting machine access to resources and metadata. Openness includes the right to deposit as well as to access, read, analyze, cite, quote, and mine. Where privacy is important to protect the rights of data providers or subjects, commons will make best-practice efforts to secure these data.

Demand sharing necessitates a radical rethink by stakeholders in their relationship with research assets produced by ‘their’ researchers, using ‘their’ funding, published within ‘their’ publications. The openness of commons allows the development of external services that can be more closed, proprietary, or involve ranking and selection: e.g. aggregation and indexing services, as long as they do not devalue the commons.

“In many instances IPRs [intellectual property rights] appear to be privatizing and commoditizing—“enclosing’’—socially useful knowledge that, if widely shared, could result in more affordable and accessible medicines, scientific research, educational resources and climate technologies. In recognition of this reality, EU policy ought to empirically examine whether existing policies are sanctioning severe opportunity costs. By recognizing contemporary technological and economic realities, EU policies could unleash moves towards more affordable health systems, wider uptake of green technologies, a more open, participatory creative culture, and more responsive democratic governance” (The EU and the Commons 2015; Accessed April 12, 2020).

Any person, organization or other entity can make scholarly work public.

As long as the criteria for open sharing are met—as determined by each commons through their governance—making a work publicly available is considered publishing a work. Additional requirements to add value for reuse (metadata, provenance, reproducibility, etc.) increase in value in a demand-sharing science culture, where outputs can be mined, mixed, and repurposed. Any person, organization or other entity (including publishing companies or entities that currently act as such, e.g. scholarly societies) will be welcomed by commons for providing services that help the publication, preservation, dissemination and assessment of scholarly work, as long as these services and the outputs they produce comply with the demand-sharing principles of the commons.

As commons content is stewarded by commoners within the commons, exclusive rights to this cannot be sold within or without the commons. Fees for additional services designed to maintain shared resource availability can be managed within the commons. Fees for access outside of the commons, and outside of the larger networked commons endeavor can be used to fund commons expenses, but cannot restrict access to content inside the commons. Operational principles for those who provide infrastructure for a global scholarly commons network are laid out in Bilder et al (2015).

Science commons explicitly reject the current model of publishing scholarly works which emphasizes the release of works only when they have undergone the peer review process. In a commons, a published work may be a version of a work that gets subsequently refined, similar to the way that open source software is released and then refined. Additional layers of curation, peer review and editing will be performed on works as needed for purpose. Note that these comments, discussions, annotations are themselves scholarly objects.

“Building a small ecosystem of capped returns is all well and good, but it won’t make much of a difference in the grand scheme of things. This idea has the most potential for impact if it becomes the new norm and displaces indefinite returns significantly – maybe entirely.” P2P Wiki

“Effective institutions at all levels require continuous engagement, because they all unravel over time.” (Benkler 2015)

“A country, after all, is not something you build as the pharaohs built the pyramids, and then leave standing there to defy eternity. A country is something that is built every day out of certain basic shared values.” Pierre Trudeau.

“Communal values must be taught, and renewed, continuously.” (Peter Linebaugh 2014)

Sustaining scholarly commons: There is global commitment and participation in long-term viability and preservation.

A global community is needed to actively grow and sustain science commons in the long-term. The shared-pool resources of hundreds of commons across the planet will need, and should command public funding for maintenance and growth. Each state has a stake and role in preserving and promoting the active knowledge available through commons repositories hosted in their territories, for the benefit of all science.

Commons will not flourish without the participation of commoners within the commons and also as citizens of their polities, promoting commons norms and values across societies. Commoning as a feature of scholarly work needs to be taught at all levels, practiced in everyday work, discussed and improved through reflexive innovations, and celebrated across the globe.

“An economics of abundance seek out these kinds of strategies of providing for our needs; it is not an economics that assumes that abundance exists, but one that analyzes modes of scarcity generation..., and that points out ways to counteract them” (Hoeschele 2010).

“It seems like if we could re-frame the way we think about these problems, and find new abstractions, new places to stand and see the issues we might be able to break through at least some of those that seem intractable today. How might we recognise the unexpected places where it is possible to create abundance?” (Neylon 2015); Accessed April 9, 2020.

All activities and outputs that take place within commons have a permanent home in these commons and are available to the public.

All content and services in scholarly commons will be publicly shared. All resources are openly available and may not be removed. There is, of course, a differential built into the amount of prior learning that enables various uses for commons resources. Mostly this is a built-in feature of the complexity of the research endeavor, and the extreme complexity and emergent qualities of the current state of research in any field. A chess club may be open to all, however, the skills needed to play well with the most advanced members become available mainly through long-time learning and practice. Similarly, optimal use of scholarly commons resources very often requires years of training/learning. See also: Against Exclusion: open is open to all.

Currently, academic research is surrounded and interpenetrated by an economic logic that manufactures scarcity as a means to grow arbitrary value and improve profit margins. The academy needs to grow its own digital economy. And for this, it needs to capture the value that researchers invest into it. One part of this exchange value will come from the expansion of internet-enabled services, another from the increase of its digital resources, and a third from the contributions of scholarly talent and funding sources.

While it is tempting to try and capture more value for pooled resources inside a commons by creating a differential use license that restricts use outside of the commons, there are more effective means available for this purpose. Commons can create or participate in civic trusts, set up like land trusts are today, which hold key aspects of the property rights for commons resources, and can negotiate with other commons and with external interests for the use of these resources (See: McDonald 2015; Accessed April 10, 2020). Cultural practices that support demand sharing within commons can also be effective in reducing behaviors such as extracting resources from the commons or seeking advantages by working with external interests.

“With ‘subtractive’ resources such as fisheries, for instance, one person’s use reduces the benefits available to another. High subtractability is usually a key characteristic of common-pool resources. Most types of knowledge have, on the other hand, traditionally been relatively nonsubtractive. In fact, the more people who share useful knowledge, the greater the common good” (Hess and Ostrom 2009).

Demand Sharing means that the use of commons resources cannot devalue these

The logic of scholarly commons starts with the notion of abundance. One mission of scientific commons is to manage a full range of science objects, without needing to reject some because of an arbitrary constraint on capacity or a responsibility to judge their value. The aim then is to maximize the usefulness and usage of these objects by supporting discoverability, mining, sharing, and reuse. Unlike natural resources (a fishery, a forest, etc.) the digital objects in scholarly communication are anti-rivalrous. Their use by one member does not devalue their use by another. Overconsumption is not a concern. The optimal state for the global network of scientific commons is one that supports as much consumption of their resources as is technically possible (See: Abundance).

In any one commons, the active sharing of resources, and the added opportunities for creative conversations and “cerebrations,” produces a great variety of outcomes and records, and new generations of results that had been enabled by the commons, and that get returned to start new cycles of knowledge building and knowing. Every commons anchors deeply into shared infinite play. Each commons reaches out to other commons to expand the horizon of the infinite play of science. In some ways, each commons is self-sustaining: a crucible of activity and joy that fuels itself.

Do-ocracy: “Responsibilities attach to people who do the work, rather than elected or selected officials.” P2P wiki.

There is an expectation of service by Commoners to support research and scholarship in the Commons.

Commons will establish their own forms of do-ocracy. This is a generalizable feature of self governance for any scholarly commons. Leadership will be gathered from the edges, where new working groups will be building and expanding the collection and its services. Effective group leaders will find that their service opens up new doors for greater service (no good deed goes unpunished). A reputation for accomplishing significant work will form the basis for participation in leadership roles.

Scholarly commons build on a tradition of service; scientists have been gifting their research results to the republic of science for centuries now. The types of activities that constitute service are expected to be enlarged and their capacity for documentation enriched, e.g., participation in online conversation forums. As a general rule, individual scholars and teams will always receive more value from their commons than they contribute. This primary surplus of value is not just due to the network effects of commons, but also from the added opportunities for serendipitous interactions. The value proposition that each vibrant commons represents can be expressed explicitly on an individual, institutional, functional, disciplinary, national and global basis.

“Single loop organizations fix problems... Double loop organizations fix problems and fix the situations that caused the problems” (Shirky 2011; Accessed April 12, 2020).

“[P]rofessions have specific ‘internal goods’. They include truthfulness, analytical skill, and buying into the professional’s fiduciary duty to their client in the wider context of behaving with integrity to all. To acquire such internal goods of practice—or ‘goods of excellence’, as he subsequently termed them—MacIntyre [1984] argues that one must practice at least three virtues: justice, courage and honesty.
When practising one’s profession, one can’t make up one’s own facts. And a good argument is one that would persuade the best of one’s colleagues, not just one’s own side. Thus, just as Francis Bacon proposed—sublimely—regarding the growth of science, that we cannot command nature except by obeying it, so the professional must master and obey the imperatives of their discipline to gain access to the agency it offers. This idea of engaging with an external or objective order implies justice, which is secured only by allowing correctness within the practice to trump ego or power. This, in turn, implies equal treatment for equal merit within the terms of the practice” (Gruen; Accessed April 14, 2020).

Commons become community-based value-generators for the work of their members.

The way forward requires an effort that spans the entire practice of scholarship, from intellection to publication. Researchers face the task of redesigning the scholarly workflow, while they inject these new modes of doing research and publication into the broader academy. The life of a scholar is rigorous and difficult, but also includes opportunities for personal and collective fulfillment. As commons spread across the academy, these will generate local communities that do two important tasks for each member: the communities cascade collective meaning into scholarly practice at the team level, and they support cultures of caring and kindness, and trustful events and friendships. They hold shared, internal virtues (goods) as binding on their members. As MacIntyre (1984) reminds us: “[T]he essential function of the virtues is clear. Without them, without justice, courage and truthfulness, practices could not resist the corrupting power of institutions.”

Virtues and normative practices in a commons are promoted to stimulate behaviors that support the production and dissemination of the best scholarship and science. They encourage respect for the principles of these commons, and they discourage behaviors and practices that inhibit participation. They apply to all stages of and participants in the research cycle. They respect and support non-standard research outputs (such as datasets, software, methods, null-results, ideas) and para-scholarly activity (e.g. leadership, community service, peer review, and adjudication).

Researchers across the globe will have wide variety of local issues to bring to their commons. The academy today is broken in various ways that reflect cultural issues locally. Each organization and discipline faces their own version of these dysfunctions. While all solutions are ultimately local, every commons creates helping practices that can be shared laterally across the planet.

“What I call software collapse is what is more commonly referred to as software rot: the fact that software stops working eventually if is not actively maintained. The rot/maintenance metaphor is not appropriate in my opinion because it blames the phenomenon on the wrong part. Software does not disintegrate with time. It stops working because the foundations on which it was built start to move. This is more like an earthquake destroying a house than like fungi or bacteria transforming food, which is why I am trying out the term collapse” (Hinsen 2017: Accessed April 13, 2020).

“How do we ensure that the system is run “humbly”, that it recognises it doesn’t have a right to exist beyond the support it provides for the community and that it plans accordingly?” (Bilder et al 2015).

Scholarly commons exist outside of specific technologies, funding sources, and business models

Scholarly commons accommodate, facilitate, stimulate and adapt to any developments and technologies that promote their goals, and enable their practices. Because the needs of commoners and the means to meet these will be emergent, commons must remain nimble and able to pivot. Commoners are stewards over their shared-pool resources. They treat these with the same quotient of caring that they bring to one another. Commoners are maintainers as well as innovators.

Commons can also fail. They can lose the capacity to own and steward the shared pooled resources they need; this will happen should their resources become “enclosed” by another organization (e.g., when these are sold to a for-profit concern). Or, a commons can fail to sustain their governance model and lose the necessary volunteer support and membership. When a commons fails, it is important that pooled resources remain in the civic trust, so that a new commons can be generated as needed to manage these. A single civic trust (Accessed June 12, 2020) can hold the property rights for one or many commons, each of which has access to the shared resource. This also reduces the transactional value of the commons, since the commons owns mainly the right of access (including mixing and mining, etc.), and the trust owns a portfolio of other rights. The trust guards against enclosure of the commons’ resources, a major cause of failure.

Other ideas/questions about commoning in the academy:

  • A single object—say a dataset—in one open repository can be claimed as a resource by more than one commons, as long as each commons supports their own dark archive, or points to a collective dark archive, in case of data loss.

  • Scholarship needs to be fearless in its infinite play. One role of academic tenure was to protect this condition. In the face of the neoliberal market, tenure has failed in this role. Hundreds of thousands of scholars will never achieve tenure. Can the commons provide this protection?

  • Someone noted that many science data objects are “uncommon” objects that require knowledge and knowhow to use and share. Scholarly commons also maintain knowledge and knowhow along with its shared resources. Commoners are maintainers too.

Moving ahead

The real question is how to rescue (or re-place) current academic institutions using commons-based societies and economics. The commons is not an alt-academy, it needs to refactor existing organizations, where possible, and spin up new ones as required. How can we help move this process forward? If commoning is the “WD40” to release science for the sclerotic hold of its 19th Century institutions (See: Is my learned society obsolete?), internet-based technology is the duct tape needed to help these hundreds and thousands of commons communities work in concert across the globe. The internet holds the future of science. Shared resource platforms, such as Zenodo <> can operate at a global scale when needed, and support thousands of small teams as required. For commoning to gain traction in the academy, we must explore commoner-owned online platform cooperatives (Smichowski 2016) as a generative practice for open science.

The first step is culture change: we need to unleash a more profound understanding of the circumstances of scholarly commoning by building up new open practices, informed by fierce equality and demand sharing. These efforts will be localized and applied as needed to yank local institutions away from hierarchy, intellectual property wrongs, and the pull of the margin that preempts ethical decisions and norms.

A text with some history

This above text originated from an early-draft document entitled Principles of the Commons, put together by various contributions to Force 11 working groups over the past six years. That draft version of the Principles of the Scholarly Commons was based on the workshop Re-imagining Scholarship held by FORCE11 in Madrid, Feb 2016 and further refinement by the Scholarly Commons Working Group. The original Google Document for this was the product of contributions by several people. You can check in on the current work of this endeavor here: <>. Hop on and add your ideas.

The text has been highly revised and edited to introduce the central tenets of academy commons and commoning for this Handbook. PLEASE NOTE: The text no longer expresses the specific recommendations nor the wording of the Force 11 document.

Open Science needs Online Organizations

First let’s look at community

All of the organizations, the teams, and the networks of science work inside an envelope of community. Community is the container for organizational culture. Commons are governed by and for their community. Learned societies foster community. Agencies fund community (sometimes). So getting a handle on community is a good first step.

The role of community may be the most important, and least understood, aspect of developing and sustaining knowledge-sharing activities. It would not be an understatement to claim that knowledge sharing rests as much on community as it does on technology. To understand why this is so, it is important to understand that community is two things at the same time:

Firstly: community represents a social/cultural container, it describes the cohort, and defines the membership for a group. This is the main way “community” is used and misused.

Note: “community” is not a convenient euphemism for “database.”

Secondly: community also describes the qualities of cultural interaction within this group, a shared sense of belonging and trust. The amount of community in a group determines the level at which individuals will voluntarily support the goals of the group. This second sense of the term “community” is what people are talking about when they propose to “build community”. Building more community into an organization or group gives each member a greater stake in the collective goal.

To makes things clear, let’s agree on terminology for the following section. The term “community” will be used to describe the social container and “community-sense” to describe the quality of shared belonging and trust within the group. A community is a group where the members share community-sense. A “weak” community is a community where the community-sense is low and a “strong” community is one where community-sense is high.

Community Sense

“Community-sense” is also a term used in social psychology (McMillan and Chavis 1986; Chipeur and Pretty 1999). Community-sense is what Wenger calls the “community element” of a community of practice (Wenger et al. 2002). On the sociology side, community-sense also implies membership and consequent obligations, practical and moral. Community-sense provides the impetus for the informal community sanctions that help prevent “free-riders” from benefiting from the work of the community (Thompson 1993). Community-sense “cascades meaning” (See: teamwork, below) across the community by enabling internal goods: virtues that are expressed through normative practices at various organizational levels (Buckingham and Goodall 2019). Communities are societies that can say “People here do things like this. Come on and join us if you can agree.”

Community-sense is the engine for social capital (Putnam 2000), for shared trust (Fukuyama 1995), shared identity (Marcus 1992), shared intimacy (e.g., friendship) (Giddens 1991), and reputation (Rheingold 2002). On a grander scale, Anderson (1983) uses an “imagined” community to describe national societies, while the Drucker Foundation (Hesselbein, et al. 1998) posits that community-sense is the answer to many current social problems. Caron (2003) also notes that communities may not be universally positive in their social consequences (remember Jonestown). 

There is also a growing literature on community (Koh, et al. 2002, Smith and Kollock 1999), and community-sense (Blanchard and Marcus 2002) for virtual organizations, online networks (Cosley et al. 2005, Butler et al. 2007), and weblogs (Broß, Sack and Meinel 2007). Most of these apply some aspect of knowledge management (Finholt, Sproull and Keisler 2002) or social science (e.g., motivation research (Cosley 2005), emotions (Tanner 2005)).


Much of the literature on effective teamwork also points to shared values and meaning. Community and community-sense act at different levels at the same time in your organization. Your university campus has (one can hope) intentional community-building efforts to promote shared meanings across all of its groups. At this level the meanings/values may be global and broad: “advancing knowledge,” “promoting public literacy,” “enabling the next generation of scientists.” These help to elevate the amount of shared meaning across the organization. You are all in this together. These meanings cascade through the levels of your organization, with each level privileging practices most meaning-full for their work. The main locus of culture and culture sense for you is not the whole organization—although this top layer is important over time—rather it is the group you interact with daily: your team, your lab, your department. Here is where community valorizes (or not) practices of kindness, courage, honesty, and justice (MacIntyre 1984).

“If one wishes to distinguish leadership from management or administration, one can argue that leadership creates and changes cultures, while management and administration act within a culture” (Schein 2010).

Double-loop Governance is the launchpad for open science collaboration

Steven Johnson [Accessed 1/15/2020], uses the metaphor of “liquid” to describe the optimal network environment to enable innovation (Johnson 2011). “Solid” networks are too stiff to pivot toward “the adjacent possible” where new ideas sprout. “Gas” networks are too chaotic. “In a solid, the opposite happens: the patterns have stability, but they are incapable of change. But a liquid network creates a more promising environment for the system to explore the adjacent possible.” (Ibid).

More specifically, liquid networks—and the academy organizations that create these—enable individual researchers and teams to explore the adjacent possible; “When the first market towns emerged in Italy, they didn’t magically create some higher-level group consciousness. They simply widened the pool of minds that could come up with and share good ideas. This is not the wisdom of the crowd, but the wisdom of someone in the crowd. It’s not that the network itself is smart; it’s that the individuals get smarter because they’re connected to the network.” (Ibid). The room makes everyone smarter; these new everyones make the room smarter. You need to find/build that room. When you do, you use demand sharing to pull the information and knowledge you need right now to move ahead in your research.

The liquid network is another way of talking about network diversity, the optimal mix of strong ties, weak ties, and strangers in direct communication (See: Ruef 2002) that is a key predictor for innovation in the global elsewhere your research can call home. How do you get this home? The most reliable starting place is to build a culture of organizational learning into your organization. Double-loop governance is a durable platform on which to develop liquid networks across the academy, or in your lab or your department, and at your learned society.

“Double-loop” governance is one example of the type of organizational governance you can establish within the open science culture of academy organizations. Here are some some ideas and some suggestions as to why you might want to consider this form of governance as the heart of your network, laboratory, department, school, college, university, science funding agency, learned society, scholarly commons, etc.

A double-loop governance system brings the values, the vision, and the underlying assumptions of an organization into an open and transparent decision cycle. Double-loop governance is an application of (and a facilitator for) organizational double-loop learning (Argyris and Schön 1978; Argyris 1977; Accessed September 15, 2020). As you will see, learning is not just a public good produced by the academy, but a logic that can create open, innovative governance for the academy.

Four practices at the heart of double-loop governance

This sensemaking/decision process is characterized by the following:

1. distributed (shared) participation and control;

2. free and informed choices;

3. public testing of evaluations; and,

4. an ability to manage conflict on the surface of discussion threads.

Members in an organization with double-loop governance have the ability to redirect, refocus, and recommit to the values and the vision of their organization. Double-loop governance creates actual peers for a peer-to-peer network. Membership is well-defined, and provided with responsibilities and rewards.

Double-loop organizations on the open web tend towards do-ocracies and value contributions over any other kind of clout. Decision-making—to the level of deciding underlying assumptions—is distributed rather than top-down. Contributions to decisions and work toward goals (software code contributions, etc.) can be used to measure the value of members, and to reward their service. The metrics for acquiring merit are ideally well-described and collectively fashioned.

“These studies [of peer communities building software and platforms] describe governance structures grafted over these systems: usually meritocratic—mostly “do-ocracy” (government by those who show up and do the work); heavily consensus oriented (but requiring only rough consensus rather than creating a veto-rich environment of absolute consensus, and only among those who do the work and show up); a substantial degree of irreverence; redundant pathways so as to avoid conclusive decision making; rare use of formal processes; never of law or managerial fiat” (Benkler 2013).

A great example of this is StackExchange. Clay Shirky (available at, describes how StackExchange uses double-loop governance to engage its members. Double-loop organizations on the web are better able to discover and reward emergent leadership and harvest the long-tail of community participation.

Much of the added value of a double-loop governed organization comes from the quality of interpersonal interactions and shared culture and meaning, the extra amount of available trust, and the additional flexibility that distributed decision making provides. This value does not arrive without additional costs (which are described below). For ventures that are designed to solve a single problem and then end, these costs may not be appropriate. But for academy organizations looking to grow creativity and innovation across decades, the returns on these costs are significant.

A closer look at double-loop governance

The Handbook didn’t invent double-loop governance. So let’s look at the history of this notion. It will not take long before you can start to imagine the activities that will drive new governance practices for your organization. A bit of jargon here. Sorry. Double-loop governance puts into practice what Argyris calls a “Model II style” of “theory-in-use”. A “theory-in-use” cultural practice describes the actual logic behind actual decisions, as opposed to any announced (but not actually performed) theory. Do not let the terminology trip you up here. Theories in use simply describe the way people really make their decisions, even when they claim to have other reasons and rationales.

Theories in use

A “Model II style theory-in-use” cultural practice is characterized by valid information, free and informed choice, and internal commitment (Smith 2001). Model II supports double-loop learning: an ability to question an “organization’s underlying norms, policies and objectives.” (Argyris and Shön 1978; quoted in Smith 2001). This ability—which all nimble online organizations require to keep up with changing codes and capabilities—needs to be established as a cultural goal of the organization. And for this, it needs to be a visible part of the organization’s governance scheme. You’ll find out how to do this very soon.

A Model I “theory-in-use” for Argyris (1982) represents a set of strategies—personal and social—that include: control over the actions and emotions of others, maximize winning among peers, and rationality as an alibi for maintaining the status quo. These strategies activate mostly unexamined (and usually unexaminable) assumptions, creating everyday practices while actively resisting reflection. “[I]ndividuals must cover up that they are acting as they are…. In order for a cover-up to work, the cover-up itself must be covered up” (Argyris 2004).

Various experts have described the “covering-up the cover-up” aspect of interpersonal relationships in companies (and universities, etc.). They all point to this as the primary obstacle to any transformative change. Until hidden assumptions (personal and institutional) are revealed and understood, any other changes are temporary and ineffective. Finite-game theorists (such as Carse 1987 and Sinek 2019) describe this situation as a role that the person forgets is only a role; like a stage actor somehow becoming the character they play on the stage. In Leadership and Self-Deception (2010), this hiding is a fundamental betrayal of the humanity of other workers; a betrayal that puts the leader “in a box” where they can only blame others, and treat them as less-than-human. In sociology this situation describes the unreflective “habitus” (Bourdieu 1990) that individuals acquire from their cultural surroundings. The psychologist Carl Rogers (1961) calls this the “social self” that the child builds to defend them against outside threats.

An individual learns Model I “theory-in-use” strategies and covering-up in childhood—it’s common wherever parenting techniques teach conflict avoidance— and throughout life they become ever more skilled at these strategies. This is one reason why change (personal or organizational) is much more difficult than simply announcing the desire to change. Individuals are skilled at resisting changes; at an active disregard for what they do not wish to know. As Argyris notes, most people caught in Model I excel in “skilled unawareness and skilled incompetence” (2004).

“Skilled incompetence,” Argyris (1986) claims, is why clearly stated plans executed with skill might end up failing to meet their goals. The reason is that the actual, but hidden (with the hiding also hidden) goal has been met: conflict has been avoided, nobody in a position of power got upset, and a whole list of unspoken assumptions remained unnoticed, and their unnoticed condition remained unspoken about.

The stated goal, perhaps a restructuring of the office work, or a whole new way of communicating decisions, never really had a chance. Model I “theory-in-use” strategies promote and enable only a single loop of learning. Basically your organization continues to get better at incompetence, to excel in unawareness. There is change possible in this loop, as long as this change supports emotional control, opportunities for winning inside the loop, and a default to reasonableness. Over time, change can be visible. Take a look at the Ford Thunderbird image below.

How much skilled incompetence is visible here?

Single Loop Management

Model I “theory-in-use”-based organizational management provides a single loop of internal communication and learning. Goals, strategies and techniques are attempted and their outcomes evaluated. On the basis of this evaluation, new goals, strategies, and techniques are attempted. All changes happen within the loop. The goal is to improve results using the current methods. This is essential mid-Twentieth Century business management guidance. How business was done.

This is also, in part, why so many Twentieth Century corporations are no longer here. Disruptive innovation and other rapid market changes cannot be addressed through efficiency alone. John Kao (2002) describes it this way, “We all want benchmarks to get the job done more efficiently. But this does not lead to disruptive, game-changing innovation, the stuff of which organizational renewal and competitiveness under conditions of uncertainty are all about.”

Government science agencies are also good examples of single-loop governed, problem-focused, service-delivery organizations. They work under externally-mandated goals and priorities. Even their single-loop quest for greater efficiency is sometimes constrained by legislative demands and regulatory road-blocks. These constraints provide a motivation for agencies to partner with double-loop online organizations, or double-loop universities. Agencies can borrow disruptive innovation capabilities built into these external groups. The hope, however, for open science, is that these partnership can also seed culture changes inside science agencies.

How to grow a double-loop culture in your university or agency

Tony Hsieh is famous for saying “your culture is your brand” (2010). Your vision statement, including your core values, is the center of your organization: “We believe that it’s really important to come up with core values that you can commit to. And by commit, we mean that you’re willing to hire and fire based on them” (Ibid). When “your governance is your culture,” the members can more fully commit to the organization. This makes many subsequent (and consequent) tasks that much easier.

Done well, culture is not just an asset, it is an engine for double-loop learning within the organization, and that, in turn, is the engine for shared knowing. Lehr and Rice (2002) make the following observation; “Double-loop learning is where knowledge is generated from information.” Done poorly, culture becomes either decorative or punitive (something that employees are required to memorize). Vision and value statements can and should be early Loop 2 outcomes.

Single-loop organizations also have vision statements. What they lack is the built-in capability to question the underlying assumptions of these. Charters, statements of values, and constitutions are all indicators of double-loop governance, although the amount of double-loop capabilities rests in how much reflexive authority they give to members.

Vision and action

The vision statement, as Sinek reminds (2009) us, is the public statement about why an organization exists. Mission statements/business plans are Loop 1 outcomes. The mission statement tell us how the organization “intends to create [the] future” (Ibid).  The “how” is firmly in Loop 1. This is further articulated in business and strategic plans, and then in policies that direct activities.

The “why” lives in Loop 2, and is embodied in the values expressed through the vision statement. The why—the vision, expressed as values—is often described as the “culture” of the organization. Beyond the “why” in the academy, we find a host of “just causes” (Sinek 2019). These are imbedded into the research of every scholar, and into the process of teaching new scholars to learn more about through infinite play. Just causes are the “why” imbedded into the thirst for new knowledge, the promises of new understanding, and the joy of discovery. Above we saw that learning is integral to culture-making. There are models for learning that apply directly to intentional learning (and knowing) within organizations.

Schein (2010) puts it this way: “When we examine culture and leadership closely, we see that they are two sides of the same coin; neither can really be understood by itself. On the one hand, cultural norms define how a given nation or organizations will define leadership—who will get promoted, who will get the attention of followers. On the other hand, it can be argued that the only thing of real importance that leaders do is to create and manage culture; that the unique talent of leaders is their ability to understand and work with culture; and that it is an ultimate act of leadership to destroy culture when it is viewed as dysfunctional.”

“Human beings learn their theories-in-use early in life, and therefore the actions that they produce are highly skilled. Little conscious attention is paid to producing skilled actions; indeed, conscious attention could inhibit producing them effectively. This leads to unawareness of what we are doing when we act skillfully. The unawareness due to skill and the unawareness caused by our unilaterally controlling theories-in-use produce a deeper unawareness; namely, we become unaware of the programs in our heads that keep us unaware. The results are skilled unawareness and skilled incompetence” (Argyris 2004).

Double-loop governance defeats self-deception and clever leadership

The main obstacle to culture change is the built-in, skilled incompetence of leaders on your team, in your department, at your lab, heading up your college, running your agency, or funding your research. It turns out that most of us (this is widespread) have gotten really good at sabotaging efforts to change the culture of our organizations. The bulk of business leadership literature seeks to address this. You don’t need to go there.

Brené Brown, speaking on a recent podcast , put it this way: “The biggest barrier to effective teams is not professional development. It is personal development. And to put it in the most bluntest terms I can, people do not take care of their shit. People are not doing their own work on what it is that gets in the way of them fully showing up as the kind of people we need in teams and the kind of leaders we need. It is what makes or breaks a team and it’s what makes or breaks culture or leaders is how well do you know yourself, how willing are you to show up vulnerably in relationship with other people; learn, listen, and grow” (Slack Variety Pack Episode 22).

The idea that you can be skilled in incompetence as well as competence has probably occurred to many in the academy who have seen peers become administrators with dubious results. But that is not what Argyris is getting at. The anthropology-professor-turned-provost with zero management skills is simply incompetent. No skilling implied. However, the dean who can deftly manage a whole room of professors in order to get buy-in on a new endeavor is likely to be extraordinarily skilled at his incompetence. He has just surrendered the opportunity to open up the actual confrontation that might trigger an honest conversation as the start for the process of change. He can never move the group into implementing a new plan with full success. He simply succeeds in avoiding blame and pretending that everyone likes him: it’s the same skill he used when he was a school-boy at six years old, and he’s only gotten better at it.

What he succeeds in is not upsetting anybody in the process of announcing a program that will fail as certainly as all the others he has offered. The dean’s people skills have deflected the real issue: change will be painful. It will challenge individuals to step up and “learn, listen, and grow.” Any conversation where nobody gets at all upset is probably a conversation without effect. We will see how double-loop governance uses conflict to achieve transparency and change. You can also check out the section on congruence (Congruence) for more about how a congruent organization can help each member “take care of their shit.”

Argyris puts it like this: “The ability to get along with others is always an asset, right? Wrong. By adeptly avoiding conflict with coworkers, some executives eventually wreak organizational havoc. And it’s their very adeptness that’s the problem. The explanation for this lies in what I call skilled incompetence, whereby managers use practiced routine behavior (skill) to produce what they do not intend (incompetence). We can see this happen when managers talk to each other in ways that are seemingly candid and straightforward. What we don’t see so clearly is how managers’ skills can become institutionalized and create disastrous side effects in their organizations” (Argyris 1986).

If the worst administrators are just incompetent, and the best ones seem to be skilled at their incompetence, how does anything change? When you boot-strap a double-loop governance to your organization, the practice of doing this creates an engine of change (when needed) and a transparent foundation for stability in the future.

Five ways that double-loop governance can save your organization from itself

1. Double-loop governance makes every member a caretaker of the vision and values for the online organization. 

Your values are not just a bulleted list on your website nor a poster on the wall. They are the deep logic of why your organization exists. When you create the knowledge loop that includes questioning and reaffirming your values into every decision, then your staff and volunteers can celebrate these values. Membership includes embracing the values, and entering into the ongoing conversation about them that keeps them current and vital.

2. Double-loop governance makes a virtue out of transparent decision making.  

Transparent here means available to all members (not necessarily public). Practically, transparency includes time and place availability. Members are told when and where a decision is being made. For an online organization, this might be a set period of time to edit a certain wiki, or a set period in which to vote online. The management of critical-path decisions may (and usually should) devolve to active subgroups charged with delivering the outcomes. These subgroups need to maintain their own transparent decision process. A great example here is Wikipedia, where each entry contains the edited text, a history of edits, and a discussion page about the text and its edits.

3. Double-loop governance brings conflict to the surface. 

Conflict avoidance is a major source of “unusual routines” (Rice and Cooper 2010) in general, including those that create institutional guilt. Conflict can arise in many forms. Personal issues surrounding time commitments, responsibility and authority, and expectation management cannot be avoided through double-loop governance alone, but they can be openly addressed and resolved in a manner that promotes reflective learning among those involved. Evaluation conflict avoidance happens when tests of deliverables are either postponed, curtailed, or done in private.

Double-loop governance supports open and thorough testing, and the disclosure of competing interpretations. Conflict is rapidly promoted to the surface of discussions, where voices of dissent become available to all members. Resolution is commonly achieved through a working consensus, not 100% agreement, but something more robust than a simple majority. Conflicts over the underlying assumptions of the organization can result in new values and a new vision: the organization is free to pivot toward a novel direction at any time.

4. Double-loop governance accelerates failure to ensure success.

Remember that double-loop governance supports double-loop learning. Single-loop learning focuses on avoiding failure.  Double-loop learning focuses on using failure to recalibrate the underlying assumptions of the activity, this promotes the act of failing as a learning device, and a logic of rapid iterations of activities with open testing.  In software development efforts, double-loop governance actively supports agile development decisions. In all endeavors, the ability to fail quickly and recover takes the fear out of trying new strategies.  This almost guarantees a better final result.

5. Double-loop governance supports do-ocracy and emergent leadership. 

Good organizations find ways to recognize and reward achievements and contributions, better organizations also reach out and cultivate leadership on their edges. One of the benefits of the network effect is an ability to reach out beyond the founding team and find people who have similar interests and valuable skills. As the network expands, the chances of encountering tomorrow’s leadership improves. When these people become engaged in activities and outcomes, they need to have a clear path to leading subgroups and then larger groups, and ultimately the organization.

“Some self-veiling is present in all finite games. Players must intentionally forget the inherently voluntary nature of their play, else all competitive effort will desert them.
From the outset of finite play each part or position must be taken up with a certain seriousness; players must see themselves as teacher, as light-heavyweight, as mother. In the proper exercise of such roles we positively believe we are the persons those roles portray. Even more: we make those roles believable to others” (Carse 1987).

Double-loop governance and infinite play

All of the features of double-loop governance will assist you with infinite play. Distributed (shared) participation and control frees you from choosing a leader and following orders. Free and informed choices come out of real conversations you have, which reach deep into underlying assumptions, where complexity can only be probed, not explained. Your governance can resist rules in favor of shared values and emerging norms. Public testing of evaluations removes the back-channel maneuvers that finite game players use to “win” over others. An ability to manage conflict on the surface of discussion threads opens up the express lanes for continual changes to reflect the emergent nature of doing science as a collaborative enterprise.

Members in an organization with double-loop governance have the ability to redirect, refocus, and recommit to the values and the vision of their organization. Their organization becomes a learning resource for their own personal growth. If this does not resemble your current workplace, then you have good reasons to become an open-science culture change agent.

Open Collaboration Networks

“We need more diverse institutional forms so that researchers can find (or found) the kinds of organizations that best channel their passions into contributions that enrich us all. We need more diverse sources of financing (new foundations, better financed Kickstarters) to connect innovative ideas with the capital needed to see them implemented. Such institutional reforms will make life in the research community much more livable, creative, and dynamic. It would give researchers more options for diverse and varied career trajectories (for-profit or not-for-profit) suited to their interests and contributions”(Kansa 2014; Accessed June 23, 2019).

Build the new, refactor the old

Looking at the inventory of academy organizations, it seems clear that open science will spawn new research collaboratives in addition to refactoring existing institutional cultures. Libraries will pivot to become central info-hubs for learning across communities of interest and purpose, their collections now digital and their all-digital journal subscriptions tied to actual costs for non-profit open publishing. Ad hoc collaborations will blossom from research conversations on the open web. More focused research discussions will get backbone-staff funding to build connected networks around grand (and not-so-grand) challenges. Open, caring programs for learning and social justice with reconnect academy institutions with their locales and regions. All of these collaborations will demand time and resources and will need new career-path support outside of the old dichotomy of “teaching” and “research” (Whitchurch 2013).

The “research” endeavor will expand significantly. Institutional, regional, national, and international repositories will grow to house an abundance of data and information resources offering novel research avenues. Scholarly commons—self-governed collectives formed to optimize shared use of common pool resources, and to secure their maintenance—will range from simple teams inside single institutions, to large, inter-organizational networks that foster a new research enterprise: “Perhaps even more exciting is the prospect that the analysis of vast data collections at scale by machines and through techniques of artificial intelligence will allow us to identify patterns and correlations not visible to human eyes alone. It has been suggested that this heralds a Fourth Paradigm of science. A profound transformation is underway, shifting the capabilities and methods of researchers. This shift is apparent across the research spectrum, from climate science, through genomics, to commercial analytics in the realm of ‘Big Data’” (Hodson et al. 2018). Bigger, cheaper, faster: the coming abundance of resources will require new types of collaboration and stewardship.

Celia Whitchurch (2015) notes that these new careers, which often blend professional skills with academic research, will need to be folded into an expanded sense of mission—and new value propositions—for their academic homes. New cadres of academic professionals will be needed to fulfill the mission of open science.

Collectives first, then communities

In corporate organizational management theories, “community” (such as a “community of practice”), is useful for management as a tool to improve worker engagement, and it also makes workers more willing to share their tacit knowledge, which can then be recorded as institutional memory. “Engagement” in the corporate sense describes a positive emotional alignment of the employee with her work and co-workers.

Engaged workers are said to be more productive (there is evidence for this), and so programs aimed at increasing their numbers have become routine. A somewhat more aggressive form of engagement is called “stakeholder alignment” which looks to build engagement for a specific project. This engagement helps projects move through implementation without hiccups. In the academy, workers at a large public university, say, who are tasked with maintaining the facilities and grounds, feeding the students, providing administrative support, etc., need effective, supportive management the same as workers elsewhere. The same is true for the staff of professional scholarly associations and other academy organizations.

Retail merchants commonly do “community engagement.” This extends the notion of engagement beyond the workplace to customers, members, or service users, in the drive for brand loyalty (in this case it’s also known as “customer relationship management”). At a time when members have simple, powerful means to compare prices and ratings, forging a durable emotional alignment between the merchant and its customers becomes even more valuable. The same is true in the non-profit world where a new army of “community engagement managers” now works to keep donors loyal and their wallets open.

On the upside, the best community engagement programs support an open dialogue to improve the qualities of the workplace, or the product or service. There is a give, and not just a take here. On the down-side, the effort to promote engagement can entail a (seemingly) unending amount of emails or tweets or whatever, designed to remind workers, alums, or customers of why they need to be even more engaged.

What happens when your employees are already engaged in their own research path and teaching arena? This presents a different challenge for universities and academic associations. How do you engage scientists who are already fully engaged with their own research? They don’t need the offer of a group tour rate to cruise around New Zealand, nor another term life-insurance policy.

The first rule here is to offer services that help scientists optimize their current engagement without adding more cognitive load (Wilson 2019). Introduce them to the “club” they can join where their pain-points and research friction problems become conversations with peers. These clubs are “…based around the joint production and consumption of scholarly output among a scholarly community. This is an economics of team production and consumption in clubs or, as we style them, ‘knowledge clubs’” (Hartley et al 2019). Hartley’s clubs center on publication, but really, the next generation of academy organization needs to be club-ified.

“The web, for good or ill, through the way it transforms the economics and geography of both publishing and interactions more generally creates an assumption of access. Perhaps more importantly it restructures the economics of which communities, or clubs, are viable. First, it makes much more niche clubs possible by reducing radically reducing the costs of discovering and interacting with like minded people (those who share cultural elements). But achieving these cost reductions requires some degree of openness. Otherwise there is no discoverability, and no interaction” (Neylon 2015; Accessed July 1, 2020).

It’s a (club) collective, not a yet community: Community can/will happen later

For several years, studies of online groups revealed a wide range of “stickiness,” a description for member engagement. In general, engagement could be plotted on the usual power-law curve; a handful of really engaged members on one side, and hundreds or thousands of mostly un-engaged members in the “long-tail” end of the curve.

One genre of online groups completely broke this curve. These were the most engaged groups online, and by a long ways. Their entire membership regularly contributed content. The problem—for them most of all, and for any online community manager trying to emulate their engagement on the open web—was that these groups were made of individuals who had been diagnosed with terminal or incurable chronic physical diseases.

These online groups, numbering in the hundreds, shared personal stories about symptoms and medication advice, uploaded and argued over new medical findings, and identified sources of emotional support for members and their families. They sought answers beyond the ken of their individual medical advisors, and they collectively shouldered the news when one of their members inevitably passed on.

The feeling of “community” was evident in their mutual concern, but this feeling was not central in these groups. “Belonging” was not the goal; it was their circumstance, their fate, their bad luck. Nobody was trying to get into these groups. Yes, they grew to care for and about one another. But they didn’t join for that purpose. Members joined because the circumstances of their lives brought them to this sad place: a space of collective struggle against a common and specific foe: their diagnosis.

Research collectives are valuable and different

Let’s explore the dynamics of these groups. Each online group focuses on a single disease or condition, from ADHD to Zika. Each member shows up already fully engaged in their own private struggle. What they need and find is an online collective, a place to share what would remain private in any other circumstance. A space of mutual learning. Douglas Thomas and John Seely Brown (2011) have described these spaces in their book, A New Culture of Learning.  “Collectives are made up of people who generally share values and beliefs about the world and their place in it, who value participation over belonging, and who engage in a set of shared practices. Thus collectives are plural and multiple. They also both  form and disappear regularly around different ideas, events, or moments.” For more than a decade, the most engaged groups on the internet have been collectives, not communities. Knowledge clubs of the first order.

The global internet has two virtues: it scales pretty well up to billions of users, and it can host a hundred million independent groups. Online “communities” generally (and always when these are commercial in intent) love to grow bigger. Group size is a key metric. Belonging builds the brand. No company wants to say, “sorry, we don’t need any more customers at this point.”

On the very other hand, online collectives only need to grow to the size that optimizes the group’s collective intelligence and variety of knowledge. In fact, you know you’re in a collective when you try to join and somebody asks you what you bring to the conversation. Collectives have no long tail of non-participants. The collective may be very sensitive to an internal “signal-to-noise” ratio. The quality of participation is a feature.

To use another analogy (getting away from disease for a moment): if you joined a church congregation—note, this is a West-Coast US protestant anecdote—you’re a part of that community, even if you only attend twice a year, and toss in a bit of coin now and then. But if you also join the choir, you enter a collective, joined a club. Everyone in the choir is supposed to—you guessed it—sing. If you just stand there with your mouth shut, people will notice. If you don’t show up, someone will likely call you and ask where you are and if you are all right. There is no “long-tail” majority of choir members standing up in the choir loft not singing. The choir has zero need for a “choir engagement manager” to encourage choir members to actually sing. Singing is why members join. And if you happen to suck at it, others in the collective might encourage you to leave.

You are afflicted by science

From case notes and anecdotes collected from community managers who have attempted to engage scientists online, it is clear that science effects its “victims” (scientists) much like an incurable (intellectual) disease. Scientists commonly spend sixty or more hours a week chasing unknowns in their labs, gathering field data, or tracking down software bugs. They share a fever for knowledge and their own common foe: the specific unknown that stands between the state-of-the-science in their specialty and a better understanding of the object of their study; the peculiar intellectual challenge (disease) they have chosen as their quest and their foe.

Scientists don’t need and don’t want to join online communities to do science. If that is all your new platform or service provides, your dance floor will remain empty. What scientists need are online clubs, collectives that can amplify and accelerate their own research, and reward their contributions to new knowledge in their chosen specialty.

Six lessons so far on online science collaboration:

1. The most engaged online groups are collectives, not communities.

2. Collectives don’t follow the power-law curve.

3. Collectives form around specific issues, and common foes. They house a hunger for collective intelligence in the face of inadequate information. The driver here is a collective need to know.

4. Unlike online communities, membership growth is not a desired metric within collectives. Small can be beautiful.

5. In terms of engagement, science acts like an intellectual disease, a diagnosis of a specific lack of understanding about some object of study that drives the scientist to devote her life to discovery.

6. Scientists will already be engaged if they join an online collective, and will already be disengaged if they are asked to join an online community.

NOTE: community will show up later, because community informs and supports culture.

It’s an easy first step: support clusters

“As they work together in their cells to pursue that goal, the shared commitment of the participants to have increasing impact helps them to build deep, trust-based relationships with each other. This makes it possible for each participant to ask for help from others as they each struggle to get better faster” Hagel (Accessed June 7, 2020).

Your university, online-enabled organization, or newly refactored learned society can start on the right track by allowing members to self-organize online teams, which are sometimes called “cells” or “clusters.” Give these teams the teamware they want to use to start conversations across the internet. Help them find others with specific research interests; this is a great use of your database capabilities. Then stand back and let them work. Remind them that clusters need to be active, to get something done early, and then consider un-forming. You are not building edifices that must be protected, but rather scaffolds for better conversations that need to be torn down as soon as they might.

Clusters are a model for the future of online science collectives. They have the virtues of being free, instant, active, and nimble. They can merge with one another or diverge from their original intent as desired. They have no requirements for a deliverable, except that they reward the services of the volunteer time they spend. And so they are motivated to get real work done. They can grow to whatever collective size they need. And when their work is finished they disappear, leaving their findings in a discoverable location on a wiki, a repository, and/or published in science journals.

In a pre-internet world, funding several thousand physical workshops a year helped fill some of the need for science collectives. In the future, internet-enabled science could be based on scientist-led online-organizations that each spawn hundreds of active online collectives as these are needed.

Six more lessons learned

1. Cluster-like groups can become an important mode of online collective work across the sciences, with huge savings in time, money, and effort.

2. When funders support travel to community-run meetings that grow a culture of sharing and trust, they enable these communities to host their own online collectives. Funders will save hundreds of millions of dollars by NOT directly funding workshops.

3. Each additional cluster can be started with a zero marginal cost (based on existing support for backbone community organizational tools and services).

4. Funders and community staff coordinate among these clusters to amplify the impacts of their results.

5. Funders encourage cross-community online clusters for trans-disciplinary science.

6. Funders can target some travel and other support to improve diversity at the community level. Staff work to nudge diversity at the cluster level.

Platforms and Norms: There’s a commons in your science future

Science is broken: Who’s got the duct tape and WD40?

Let’s review a bit: Scientists are individually infected with their own science quest. Science is really social too. Why else would scientists take a hundred-thousand airline flights a year to gather in workshops and solve problems together (well, apart from the miles)?

The next bit needs to be about culture and technology. But not so much about the content of culture and the features of technology. Rather, about the doing of culture and the uses of technology.

Yes, the sciences are broken. Some part of this rupture was built-in (Merton, who outlined scientific norms in the 1940s, also outlined the integral tensions that disrupted these—i.e., the Matthew effect). But much of the damage has come from the displacement of the academy within society that has warped the culture of science.

Yochai Benker generally describes the tensions of this warping as “three dimensions of power”. These power dimensions (hierarchy, intellectual property, and the neoliberal need to always show more returns) work against science as a mode of peer production that self-commits to shared norms. Science needs to find alternative means to fight hierarchy, share its goods, and own its own returns.

The sciences are stuck and fractured, in need of both WD40 and duct tape—culture change and technological support. Scientists need to operationalize open sharing and collective learning. For this, they must discard the institutions that enable the above dimensions of power in favor of new communities and clubs that can house cultures of commoning, and activate global peer production.

The Power of Demand Pulling: a review

The book, The Power of Pull, (Hagel, et al 2012) announced that the internet had changed the knowledge-world of smart businesses; effectively flipping this from a knowledge pushing activity, where companies used internal knowledge resources to create innovation, to a knowledge pulling activity, where companies harvested knowledge from a broader network of sources and assembled this in novel ways to create innovation. This book was co-written by JS Brown, who had been the head of Xerox PARC, one of the largest knowledge-pushing sites around. This knowledge flip notion seconded what the Cluetrain Manefesto <> folks had announced in 1999: customers were getting smarter than companies.

In the academy, however, it was business-as-usual, or as usual as might be describable for an early enlightenment project housed in late medieval organizations (universities). In a 2010 blog post for the Society for Scholarly Publishing, Why Hasn’t Scientific Publishing Been Disrupted Already?, Michael Clarke wrote, “When Tim Berners-Lee created the Web in 1991, it was with the aim of better facilitating scientific communication and the dissemination of scientific research. Put another way, the Web was designed to disrupt scientific publishing. It was not designed to disrupt bookstores, telecommunications, matchmaking services, newspapers, pornography, stock trading, music distribution, or a great many other industries…. And yet it has [emphasis in the original].”

Clarke’s point is that scientific communication should have been the very first arena for radical change fostered by the new opportunities afforded by the Internet; and certainly not the last. His conclusion on why scientific communication had not (in 2010) changed at its core—despite the efforts of the open-access publishing (and open-science and open-data) movements—was that publishing had become embedded into career advancement based on the older model (dating back originally to 1680); nobody cared to be the first martyr to a new system, while the old system, however inefficient and expensive it might be, still got one tenure. In short, the reputation of the scholar was carried on the shoulders of the reputations of the journals that publish her works. Journals had become the standards—heraldic gonfalons—that tenure-seeking authors could fly for their deans. But no longer. As these words are being written, open science publishing efforts have pushed back against both the for-profit publishing (more precisely, for-profit “privatizing”) model and against the role of journal reputation for advancement.

As an open scientist, you’ve already moved past relying on seventeenth century publishing logics and onto open access and open review for your work. This means that your research is open to the power of demand sharing/pulling: you have realized the opportunity for others with very similar research interests to discover your work, critique and make suggestions about this, offer questions and new ideas, and push your research into an innovative matrix of collective intelligence. Those colleagues from across the planet who stepped up to respond to your work can become an ad hoc cell shaping the contours of learning toward the goals of exploiting serendipity and increasing research impact.

These small collectives are exactly what your research needs to accelerate into new discoveries; realizing the power of pull, as you activate demand sharing. At any one time you will probably find yourself a member of several internet-enabled cells, each probably numbering fewer than twelve members, and each working to solve some immediate problem. Once their problem is solved, the cell can publish the solution and simply disband. All that remains are the affective ties of trust and gratitude that its members retain, each one building their own network of collegial acquaintances, “weak ties” that will grow and mature over time.

Amassing a social network of “weak ties” is one of the most important and valuable practices that any scientist can do, not just for science, but for their own careers. “If information is to move from one group to another that is far away, either socially or physically, then the only way is through bridges—links between two different people, two different worlds, links that, by definition, are weak rather than strong. As Granovetter (has 1977) wrote in his original paper, ‘this means that whatever is to be diffused can reach a larger number of people, and traverse greater social distance, when passed through weak ties rather than strong’. If weak links are removed—the bridges, as it were, are blown up—then this would harm the spread of information more than if strong links were dissolved. If the bridges did not exist, new ideas would be stunted or spread slowly, science would be handicapped, and social divisions would be perpetuated” (Koch and Lockwood 2011).

Let’s turn now to these new “weak link” open collaboration networks, and to internal reorganizations that academy institutions face as they grow into open science hubs.

“[N]ow that science is becoming a network, knowledge is not something that gets pumped out of the system as its product. The hyperlinking of science not only links knowledge back to its sources. It also links knowledge into the human contexts and processes that produced it and that use it, debate it, and make sense of it. The final product of networked science is not knowledge embodied in self-standing publications. Indeed, the final product of science is now neither final nor a product. It is the network itself—the seamless connection of scientists, data, methodologies, hypotheses, theories, facts, speculations, instruments, readings, ambitions, controversies, schools of thought, textbooks, faculties, collaborations, and disagreements that used to struggle to print a relative handful of articles in a relative handful of journals” (Weinberger 2011).

Open Collaboration Networks are the future of science, not just open science

One thing that is new and not at all new about open science is the reliance on “online organizations.” Learned societies bring together widely dispersed scholars, and have been doing so since the 17th Century, when the advent of postal systems fostered widespread mail correspondence across the universities of Europe. Internet-enabled science organizations are, by definition, no older than 30 years.

Platform coops and gig economy firms are sometimes called “virtual organizations:” companies expanding their workforce without adding more employees, through outsourcing. As the current pandemic (this is 2020) has taught us, any organization can become “virtual” today, with all of its members separated in space and communicating across the web. So the Handbook will use “open collaboration networks” (OCN) to corral the capacity to include geographically distant colleagues into a working collaboratory that can harness collective intelligence. As we learned in Science happens elsewhere, no single college, university, laboratory, or agency has the means to assemble all the scientists doing work in even the tiniest sub-discipline. The dozens of scholarly commons that will emerge in the coming decades will all use OCNs to guide their work.

What can you do when your research team looks around and realizes, “This problem is way too complex?” The first impulse—over the past half century or so—would be to apply to a funding agency to run a workshop. Large agencies spend billions of dollars on these; they create an ocean of white papers that lurk, mainly unread, on institutional websites. Elsewhere we discuss how most—but certainly not all—workshops are obsolete today. There are nimbler methods to get the same outcome. Let’s explore.

Ashby’s Law

When your government agency or university laboratory looks to innovate in a world where multiple/large data inputs are coming on line, how can you stay ahead of the inherent complexity of the systems you are creating/interrogating? One way to look at this problem is through Ashby’s principle/law of requisite variety. A principle of cybernetic management, requisite variety notes that unless the control system has at least the variety of the environment it controls, it will fail. Which actually means that some part of the environment will be controlled elsewhere.

Elsewhere is also where innovation happens; because unless you can corral the inherent variety of the problem you face, it will seem too complex for your team to innovate a response.  You can either go out and hire a bigger team, or you can borrow enough requisite variety just long enough to bring your own team up to speed. This is one form of an open collaboration network.

The network you build to tackle a tough problem needs to have enough of a portfolio of knowledge and skills to address all parts of the problem. Not only that, but they need to communicate their skills and knowledge to one another so that each team member shares in this collective intelligence. Andrew Van de Ven put it this way, “Requisite variety is more nearly achieved by making environmental scanning a responsibility of all unit members, and by recruiting personnel within the innovation unit who understand and have access to each of the key environmental stakeholder groups or issues that affect the innovation’s development.”  (Van de Ven 1986).

Open collaboration networks include online communities of practice, research collaboratories, open source software programmer collectives, and other groups in a great variety of arenas and professions. What they offer is an open network of common interest and complementary talents. When your university, foundation, or agency is looking to innovate in a world where data are more plentiful than insights (Abbott 2009) then it makes great sense, in terms of time and effort, to fund and build an OCN and gain enough requisite variety to conquer complexity and kickstart some innovation.

So, you want to, or need to start a new open collaboration network, or build one inside the mission of your department, or laboratory. Perhaps you have been awarded some funds to do so. If so, congratulations. If you do this right, it will govern itself into a productive, happy (smiles are a good thing) academic community.

Here are Seven Key Suggestions 

1: Read Jono Bacon’s 2009 The Art of Community. Bacon has more good advice than you will find in a hundred blogs. Governance is not the same thing as management: “Don’t fall into the trap of assuming that governance is merely about decision-making. There is no reason why you can’t constrict it in this way, but you will be missing out on a wealth of opportunities to excite and energize your community.” What Bacon will also tell you, and it’s very important, is that you need to build your community and its governance first thing. This is not a “phase 2” activity in any plan.

2: Connect with the community on the issue of membership. Who gets it, what levels there are, who gets to vote, who gets to lead, and how to manage conflicts: getting some early conversations done with the community, and particularly those who will be asked to volunteer, will help to draft that part of the initial governing documents. Remember that you are setting up the initial conditions for your member-led organization. Double-loop governance means that your members will be able to rethink membership rules and roles.

3: No matter how much you want to implement a plan with your team, and no matter how you have researched effective governance, you will only be creating a temporary framework for your membership to use as a first go-around for a governance system. Because you are giving your members the ability to make changes in the documents you have drafted , you have to understand this: they will make changes, probably right away before even an initial vote is taken. And then remember: this is a good thing. So, put the texts up on a wiki and let them have a go at it. The sooner they come to own the text, the sooner they will start to celebrate its vision.

4: Put some budget into play if you have this, but never pay volunteers for their time (See below about staff and volunteers and who does what). What can you pay for? Help support communication platforms, pay for students to do some background research for a draft business plan (the “how” of your organization), bring in some key community members for a workshop, but open this up through video conferencing, and included others who express and interest to also be present. 

5: Always work toward a rough consensus, and never erase “minority reports.” Let conflicts rise to the surface and deal with them quickly. Leave their content open for others to see. Show your members that their time, their skills, and their opinions are honored, even if they are overruled. Jono Bacon has great advice for conflict resolution. 

6: Ignite some preliminary teamwork by having the initial community vote on two or three small, “low hanging fruit” efforts and then support ad hoc teams (clusters) to address these. By this you begin to show an initial innovation ROI the online organization will build upon.

7: Hold a few face-to-face meetings, but keep them from being PPT centric. Plan for small-group discussions and multiple breakouts, and hold the meetings in convivial neighborhoods, not airport hotels. Gather as many members as are there and read over the founding governance documents paragraph by paragraph (but only once, and then set up a process to edit the text online until the document goes up for a final vote), and let the group speak their concerns. Open up the entire budget for the membership to give their suggestions. If possible, let the membership vote on the budget after suggestions have been taken and changes made (a real vote).

These suggestions are just a starting point for boot-strapping a double-loop governed virtual organization. Once the hard work of building in double-loop governance into the culture of the organization is over, the rewarding work of seeing how this accelerates volunteer engagement can begin, and the creative work of husbanding this engagement into your organization’s business and strategic goals can be fully supported through the culture and the values, and the celebrated vision you own as a community.

Walking the walk is only hard when you haven’t tried it

For many commercial organizations, the rush to market and the lure of some short-term exit strategy might make all this focus on congruence and culture and values and vision seem superfluous. And if your goal is to start-up and sell your business in the next 24 months, you would be wise to stick to a single-loop management plan (with a hefty stock option, because you will not have much love or glory). Similarly, when you have funding for three years, building a governance structure that takes at least three years to find its feet might seem unattractive. The Primary Investigator in you—and certainly the Program Manager you send reports to—wants/needs to show progress in months, not years. So you punt on the governance and push for “results.” This is the pathway to innumerable white papers that nobody ever reads.

The main reason why so very many funded-project-led open collaborative networks fail as soon as the funding is over is this: their PI and the agency/foundation PM failed to enable the network community to build their own collaboration infrastructures inside a self-governing process. They have no self-governed collaborative capacity to move ahead when funding stops.

If you are tasked to build an online organization that can stand on its community-based resources, you should seriously consider building in double-loop governance from day one.  What you are offering your membership (or your employees, or your customers) is a congruent experience: whatever your brand (or your vision) will become, it will emerge directly from your culture.

Your OCN deserves all the Democracy it can Handle

“What motivates social behavior is a sense of ownership, to be able to say that the commons or a shared good is ‘mine’ or ‘ours.’ The more intensely ownership is experienced, the greater the sacrifices people are willing to make” (Klamer 2017).

As an open scientist you may be been tasked—perhaps through a grant, or by occupying a management position in your university—to build or lead an open collaboration network (OCN). Up front, you will face questions about the cost/benefit issues of democratic governance, as opposed to central management. If you stepped into an existing supervisory role, this question is yours to bring to the table as you get a handle on the exiting culture and everyday practices, and begin the process of making these practices more reflexive and transparent.

Given the usual shortage of funding and time, you will have real concerns about the effort required to build a community-based governance system. These concerns are usually layered on top of the more general concern that the community (or rather, certain activists within the community) may use the governance system to push the organization’s goals toward their own interests. Managers who desire to manage have little good to say about adding democracy to their process.

Ownership is the cornerstone of community

While demand-sharing cultural norms call for removing the barriers of private ownership of the academy’s scientific intellectual property in favor of commons-based resource governance, organizational governance is based member-ownership of the budgets, processes, and decisions made within the organization. Members own their organizations, and the organizations share their assets among members, and with other academy organizations.

But democracy is expensive in time and resources…

Certainly, democratic governance increases the overhead (in terms of time and effort) spent on governance. Top-down decision making can be quite efficient up to the point where it tends to fail rather abruptly. Democratic governance is also more prone to being gamed by people with time and interest to do so. This is where the community comes in to play. When you build in enough democracy to give the community the opportunity to really govern, it will tend to resist the efforts of certain individuals to subvert this opportunity. This is one of the goods that democracy delivers to your OCN.

What are other goods that democracy provides for your OCN? What does democracy do that you cannot do without committing to this type of governance? There are two types of goods (positive, valuable results) that democracy creates within an OCN. The first type are community building goods. Democracy is central to your ability to build a community for your OCN. The second type are decision support goods. These help align your decision making effort with the goals and vision of the community, and improve buy-in by the community.

The main community-building good you achieve by promoting democratic governance is inclusiveness. The divide between the funded OCN team and its larger collection of stakeholders disappears. Governance provides the means for a wide variety of voices to be recognized. This encourages more participation (and more participants) building the size and the depth of community for your OCN.

The next community-building good is that of bottom-up control. Bottom-up control over your OCN establishes the means for the community to have a say not only as an afterthought (e.g., a survey), but on matters as central as budgets and goals. The community is given real ownership over the OCN’s efforts. This is the hallmark of any authentically “community-based” or “community-led” organization. Until your OCN has achieved this level of community governance, your efforts to build a community will be met by a wall of indifference. Developing the means for community-based control of your OCN breaks down this wall and builds the foundation for real community growth.

Decision support goods can also be expected from your democratic governance efforts. Once the community is fully involved, you receive the benefits of their considered judgements. The whole point of your OCN is to engage with larger numbers of people with expertise. Why not put this expertise to good use?

Give intelligent people—your colleagues—the reason and means to reach a considered judgement on an issue of importance to your OCN and they will work diligently in this effort. The second decision support good your OCN gets is new leadership. Those in the community who have expertise, time, and interest will step up to take positions of authority/responsibility for the work that the community is contributing to the goals of the OCN. Find the means to reward these leaders (give them resources to manage and build their reputations within the community) and they will become the levers to take your OCN to the next level.

Of course, not all democratic governance efforts are equal. Your OCN will need to work to maintain transparency in the governance activities, in part by making the staff fully accountable to the community. Your OCN’s governance system will need mechanisms to be modified so that it can learn to be more efficient over time.

Using democratic governance to build community is probably the best solution for crafting a virtual organization that can survive its initial funding. There are no half-way democratic forms that will deliver the same goods. And if you decide to forego democracy, or delay democracy while you build your technology, there will likely be a day when the funds run out and you will turn to the “community” and ask them for their support. Good luck with that.

Why do democratic governance when you are already paying staff?

Getting the right mix of staff and members for an OCN is a crucial task for sustainability. The key is to take limited resources (if you have unlimited resources, call me) and invest these in directions that bring the best return for all. Member volunteers are the heart of an OCN . Keeping this vital organ alive and well is job one for staff. Members bring skills, vision, energy, and passion to the organization. They tend to do so in short-term increments. They need to know their efforts are valuable. This knowledge prompts them to stay engaged. Through the serial engagement of many members, certain activities are maintained: governance, oversight, incremental work on infrastructure, a supply of new ideas, and, yes, occasional sidetracks. Nobody can sell your OCN to funders and new partners better than members. And nobody can grow your organization over time and on budget like members.

What is your staff good for? Staff are the backbone of any OCN. They keep it on track and guide its fortunes. They have responsibility for those tasks that members should not be asked to perform (more about this soon). They also have responsibility to keep members engaged. They do the thank-less work and get paid for this. But that doesn’t mean the organization doesn’t owe them a heap of thanks. Still, they are professionals, and need to step up an take charge when the need arises.

Generically, the work of staff falls into two buckets: everyday necessary tasks and putting out fires. members should not be asked to perform these types of work. Staff run the events on the community’s calendar; they manage the web-presence, the accounting, the teleconferences, and a hundred day-to-day activities. They facilitate volunteer efforts. And, when the website is hacked, or the projector bulb burns out, they fix it.

Members get called in to plan and direct new activities and articulate new goals. Ideally, they are given a say (not just a voice) in the OCN’s operational budget. Because they do the planning and determine the budget, it’s only fair that they do some of the work. They can be tasked to scope out any new work required by a new goal and to build new capabilities to meet this. Then they either do the work, or determine that the job is too big for them to accomplish.

When the members are done with their efforts, the outcomes are passed back to staff to incorporate into the organization’s operational inventory. Sometimes the outcomes are not fully ready to use (having been built by members). Staff might need to hire an outside expert to polish the work. Note: this person should be fully “outside” and not a community member. Never hire a community member as a consultant to fix another community member’s volunteered contribution.

When the job is too big, members might ask for some support (more often they just stop answering emails). There are many ways to support members. Paying them is the least valuable, as this transforms them into non-members. There are several descriptions of the negative impacts of paying members. Basically you are pissing in your own soup. Other means of support are always better: find them assistants (pay for interns), pay their travel, pay for hardware and software when required, and, if nothing else works, add staff to help. Sometimes, this might mean making a skilled volunteer a “fellow” for a short period of time. This move should include a community vote, including an open call for the fellow position within the community. The community is tasked to help staff fill a temporary (less than a year) need from within their ranks. By this, the “fellow” can be paid for a time and then return to the ranks of the volunteer community.

Remember that members need to know their efforts are valuable. The organization needs to build and maintain recognition systems for members. These include online and in-person awards. The three motivations for engagement are money, love, and glory. When it comes to members, if you are stingy with the glory, don’t expect any love. And when you let the community add to their own glory, then you can stand back and watch new leaders emerge and know your open collaborative network is healthy and growing.

Self-Governed OCNs are a Win-Win-Win for funders too: let them in on this important fact.

Science agencies and science-focused foundations fund science. It’s their job. Usually this is done directly through the competitive funding of research projects, equipment, and centers. All of these competitions can include instructions for the proposers to describe how their work supports open science. You can make it clear in the request for proposals that the outcomes of the project, including software, data, and results, are openly shared, reproducible, and reusable. You can do much more than that, for a very small investment—say, one percent of your research budget. With that investment, you can help shape the culture changes needed to move your funded research into an open science future.

Funding open networks

As an open scientist in a funding agency (government or foundation) or a scientist proposing a new research network, there is something to always remember: some important science-related activities that cannot, indeed must not be directly funded in order for them to succeed. Sometimes innovation needs to go where cash doesn’t count.

If you are guiding a science funding agency or foundation, then the notion that you can achieve certain high-value science goals only by not directly funding them may be news to you. It should be welcome news. In fact there are enormous innovation potentials you can only realize when you can refrain from adding money to the mix. There is a caveat here. While you cannot fund these activities and processes, you also cannot manage them. Instead, they must govern themselves.

As the abundance of openly shared research objects grows, self-governed commons need to step up and steward these resources for an optimal return on investment. The time is ripe for new varieties of science/data virtual organizations/networks. Drawing their members from a broad swath of experts, led by the community they build themselves (through a governance they own), and powered by volunteers, these commons offer funding agencies and the academy new forums for scientific discussion, knowledge management, and collective intelligence.

To be really clear; These organizations still need support. All of them require sponsors to pay their staff and expenses. They manage websites, teleconferences, shared research capabilities, and stage face-to-face meetings to build community sense, and expand the “room” of their conversations. But these activities: the occasions for trust building, the growing sense of community, and the actual work: these are ultimately accomplished by the volunteers for themselves and without being paid (apart from some logistic support).

Distributing activities across an engaged volunteer network of peers can use a very limited budget—say five percent of the total research budget—to accomplish the socio-cultural side of the program, given time and proper attention to governance. Money is not the driving force here; trading money for time is not an option. Agencies that fund science research can easily afford to support governance through project research budgets; more to the point, they cannot afford to not support this if they want to accomplish what only a community can do.

At the same time, efforts to support “community” are often pursued without actually building community-based governance. Project budgets may include large amounts of “participant support” for annual “community meetings” that could help build engagement. But without the governance structure that puts the community in a position to determine key aspects of the core activities, these meetings are really only very expensive alternatives to an email list. The attendees may learn something, and will forge their own interpersonal connections, but the work of building community through governance is left undone.

The amount of real governance required to support activities also depends on what types of activities need to be supported. If communication is the goal, then a relatively weak governance system will suffice. If real collaborations are to be supported through an open collaboration network, then a robust governance system may be needed. Clay Shirky (2008) examines three levels of social interaction: communication, coordination, and collaboration. Each of these levels needs its own level of governance to be sustained across years.

Funders gain value for their research outcome when they invest in community. But it’s the members, the scientists and their teams in these collaborative networks that realize a massive return by their involvement. In fact, each and every scientist should get more than they give. This math is driven by a host of network effects. You, the agency manager, can finally get something for almost nothing; and that something is a thing that no amount of additional budget would achieve!

Seven science outcomes that your agency can only fund by funding self-governance

Here are Seven Outcomes your science agency can now get only by not funding them directly. Rather, you can achieve these through supporting a community-led virtual organization of scientists/technologists:

1. Your agency gets to query and mine a durable, expandable level of collective intelligence;

2. Your agency can depend on an increased level of adoption to standards and shared practices;

3. It will gain an ability to use the community network to create new teams capable of tackling important issues (also=better proposals);

4. Your agency can use the community to evaluate high-level decisions before these are implemented (=higher quality feedback than simple RFIs);

5. Social media becomes even more social inside the community, with lateral linkages across the entire internet. This can amplify your agency’s social media impact;

6. Your diverse stakeholders will be able to self-manage a broad array of goals and strategies tuned to a central vision and mission; and,

7. You will be able to identify emergent leadership and potential new employees.

Bottom Line: Sponsoring a community-led, volunteer-run science organization offers a great ROI. There are whole arenas of valuable work to be done, but only if nobody funds this directly.

ESIP: a real-life example of a wildly successful OCN

The oldest and perhaps the best example of a self-governing, online science OCN in the US is Earth Science Information Partners (ESIP), sponsored by NASA, NOAA, and the USGS ( Why is ESIP a good example of these new science organizations? A good deal of the buzz at any ESIP meeting meeting is generated by the appreciation for the “ESIP way” of getting things done. ESIP champions open science at all levels, and this openness extends to everything ESIP does internally. ESIP builds and celebrates a strong culture for the pursuit of open science in the geosciences, and remains a model for other volunteer-run open collaborative networks (OCN) across science domains. There are lessons learned here that can be applied to any arena of science.

Governance NOT Management

One important lesson learned at ESIP is this: governance must never be reduced to management. Funded projects and centers are managed. OCNs are  self-governed. Volunteer-run science organizations are intrinsically un-manageable as a whole (for reasons we shall see soon), and at their best: it’s a feature, however inconvenient for agency program managers. An OCN can certainly house dozens or hundreds of small, self-directed teams where real work can be “managed”: teamwork is central to their mission. ESIP “clusters” are good example. These teams can produce valuable and timely deliverables for science and for the sponsors.

The style of governance is also very important here. Attempts to shift governance away from the membership and into top-down executive- or oversight committees are always counterproductive. They give the membership a clear alibi to not care about the organization. Academics have enough alibis to not volunteer without adding this one. The members need to own the mission, vision, and strategies. Successful activities will emerge from initiatives that have been started independently, and with some immediate urgency, by small groups; and which grow into major efforts with broadly valued deliverables. Bottom-up governance will outperform top-down management over the long term.

Science culture shifting

Probably the largest recognized impact that science OCNs can make here—and perhaps only these can accomplish this—is to model a new, intentional cultural mode of producing science. This new cultural model will be centered on sharing (sharing is also one of the oldest cultural traits of science, only recently neglected). Sharing ideas. Sharing software, tools, techniques, data, metadata, workflows, algorithms, methodologies, null data, and then sharing results. Reuse needs to become a key metric of science knowledge (Cameron Neylon noted this at the original Beyond the PDF conference).

Transforming science means changing the culture of science. Science OCNs must perform a lot of culture-building work here. This is often a challenge for their sponsors. The key learning moments and opportunities, and perhaps the highest ROI for sponsoring a science OCN is when this community teaches its sponsor to also change their organizational culture.

Three critical governance conditions any agency/foundation sponsor needs to heed

There are three necessary conditions for an agency-sponsored, community-led organization.

1. The sponsoring agency needs to allow the community to build its own governance. Governance documents and practices are not subject to approval or even review by the sponsoring agency, apart from needing to follow standard fiduciary rules. The sponsoring agency can offer input the same way other individuals and groups do, but the community decides its own practices. The metrics for the governance are the growth of volunteer participation, and spread of community involvement, the perceived transparency and fairness of decisions, and the community’s estimation for the value of the work being done.

2. The sponsoring agency has no right to review or in any way interfere with elections. All organization members have the right to run for office and to be elected. If the governance process allows, volunteer leaders might be selected through a lottery, which helps bring in leaders from the margins.

3. The agency’s sponsorship is designed to help the organization grow into its potential as a volunteer-run, community-led scientific organization. The returns on investment for the agency are multiple, but do not include tasking the organization to perform specific duties, other than to improve their governance (and their member satisfaction) over time.

As an open scientist, and an open-science culture-change agent, you will want to be able to demonstrate that a small amount of your science funding budget is best spent on building self-governing organizations, instead of direct investments in funded projects. The real test for a science OCN is to develop fully within the scope and logic of its organizational type. The concomitant test for the sponsors is to understand that sponsoring a new and different type of organization will require some new expectations and an incubation period (a few years) of growth and experimentation to allow the organization to find its own strength and limits.

Commoners workshopping

Workshops and beyond

Workshops demonstrate how much science needs shared knowing.

There are lots of reasons to not run workshops, and a few reasons why these can be uniquely instrumental. The best workshops represent a mode of in-person shared knowing sometimes known as BORPSAT: A Bunch of the Right People Sitting at the Table.

If you can…:

  • Know who the right people are;

  • Know how to contact them and get them on a plane on the same weekend;

  • Know how to facilitate the conversations necessary to draw out their variety of knowing (Knowing and conversation);

  • Know how to record this and where to share it; and,

  • Will foster after-connections among the workshop members (without funding, of course);

Then you’ve got what you need to run a great workshop.

Workshops can be effective for synthesizing new ideas and tackling common pain points. They are expensive (in many ways), and often unnecessary. If the “right people” have other venues to meet each other and have acquired enough inter-personal trust to open up remotely, then they are ready to “gather” on the internet/teleconference.

The NSF and NIH each spent a billion dollars funding science workshops last year, and all you got was a lousy white-paper.

Significant scientific funding and scientist participation in collectives can already be evidenced in the activity of hosting scientific workshops to address important, shared issues. Science workshops are a major current expression of the value and need for science collectives. Workshops are where scientists gather in place to collectively respond to challenges they face in their research. In the future, open collaborative networks will be able to spin off virtual workshops on the internet at any time without additional funding. Today, workshops are the 20th Century model of how to gather to solve mutual complex problems.

It is likely that you have travelled to and participated one or more workshops over the past decade. You’ve met a lot of really smart people. Shared gallons of really bad coffee. Had more than a few beers after long, long days of somewhat-facilitated work. You have spent considerable time helping write reports and white-papers. Most of these papers you never saw again. A few got published. Some workshops are more successful. Some are a shambles. As a mode of collective science, there are times when a workshop makes perfect sense, and maybe always will.

Workshop worries

There is also a way—and good reasons—to make the great majority of workshops unnecessary, by funding and building science communities instead. Just as digital journal articles have acquired their granularity and an arbitrary scarcity based on the history of printed journals, workshops have acquired their own granularity and scarcity. Here are some of their limits:

  • Workshops need to have enough “work” to do to fill 1-1/2 to 2 days of effort (to justify 2 days of travel). You can’t do a half-day or, say, a twenty-day workshop;

  • Workshops need to support say 16-34 participants, and these scientists must be available at the same time;

  • Workshops get funded to explore science research topics “important” enough to justify their $40k budget.  Other collective issues and needs are not currently very fundable.

  • Workshops need to have a topic that is still an issue months after the proposal submission.

  • Workshops require moving people around in airplanes.

  • Some fraction of workshop proposals don’t get funded at all.

Workshops are a product of Twentieth Century science. Science before the internet. Science before someone figured out how to let scientists create their own collectives online at virtually no cost.

That’s right NSF and NIH funders; there is a way you can support thousands of self-organized online workshops with a net marginal cost of zero. Well… zero, that is, after investing about 20% of the current outlay for workshops to support several dozen self-managed science communities.

Above, we explored a working model for this Twenty-First Century strategy, building a commons with online collaborative networks. We have lessons already learned and ready to be copied across other research domains; a model that already supports better, more effective, and more nimble collectives than the current workshop model. That being said, you might be in a position (funded) to do a workshop. So do are really great one, where the knowing conversations flow.

Ten rules for a better workshop

An open-science workshop takes demand sharing and fierce equality into a two-day converse-a-thon, where every participant gets their say, and the entire room moves at the speed of conversation.

Here is the Santa Barbara Workshop model:

This is a time-tested model for getting the people-in-the-room to explore all of their expertise and their imaginations. It works every participant to their limit. The feedback most received from these workshops: “I’ve just worked harder and I’ve also had more fun than I have ever experienced before at any workshop.” At the end, people actually complain of “brain fatigue,” a condition you help cure with beer, or a good walk on the beach (it was born in Santa Barbara, after all). If you don’t have a beach, then an up-town walk also works.

If you are interested in doing your own Santa Barbara Workshop, you can use these 10 rules to facilitate the best workshop you’ve ever done:

RULE 1: Pick a place that’s right in town and give them dinner/lunch

Before the workshop starts, make sure you feed the participants. Pick a downtown hotel near cafes and bars. Never do this at an airport hotel, or isolated venue. If your workshop starts after lunch, feed them a good lunch first. If your workshop starts in the morning, feed them dinner the previous night. But do not try to gather them for breakfast before the workshop. People have a variety of breakfast desires. Have a table with coffee and snacks in the room.

 RULE 2: The ideas need to travel at the speed of conversation

No more than 35/36 people. Small groups all day.

The workshop planning should focus on getting a wide spread of expertise in the room, but no more than about 35 people (7 tables of five, or 5 tables of 7, or 6 tables of 6). The whole day will be used to promote critical conversations at these tables. As soon as the conversation lags at a table, give it something new to do (e.g., another question [see #4 below]).

RULE 3: Open with a blue sky session, get the creative juices going

Start the conversations with a real “blue-sky” design problem. Let everyone add their fantasy to the solution of a problem. Give them paper and markers, scissors and glue. Give them props and tape. This is the only session where there is a brief report out. Let the groups compete for the most fantastic solution. Have them map their ideas on big Post-its and then stick these on the walls of the room. This beginning session is designed to help the group achieve an open conversational mode of interaction.

RULE 4: Give them real questions to answer, and let them add to these

After the blue-sky exercise, each table is given a question to tackle (not necessarily the same question, although most tables might end up answering every question). In the weeks before the workshop, spend real time coming up with 10-12 key questions. Map out how the answers to these add up to a larger picture. Rank these questions as “central” or “if time allows”.  Create some colored sheets of paper that say “Hot Topic” on them. Give each table a few and encourage people to create their own question. Give these questions to OTHER TABLES. Never let someone answer their own question. Some questions will be better answered by tables with specific expertise, others by tables of mixed expertise (see below). Credit: This rule was provided by Susan Colitan, Vice President of the Paul G. Allen Family Foundation. The better the questions the more knowledge you will extract from the workshop!

RULE 5:  Break up groups 2-3 times over the course of the day and vote with your feet

Give each member a name tag (first NAME on both sides). This tag should also tell them which table at which to sit (designated by color, number, animal, etc.). You might want to start by mixing up the expertise at each table. For example the COLOR designated tables might include a technical expert, a managerial expert, some content domain specialists, and others. After a couple hours, have everyone switch to the NUMBER table, which might be grouped by expertise. Later, they might switch to an ANIMAL table, etc. At the end of the day have a final question back at the original table. At any time anyone can move to a different table. This is called “voting with your feet.” Announce this at the beginning and also every time your swap out table designations.

RULE 6: Use big paper Post-its to gather ideas

The table conversations need to be captured first on big Post-its. Have the table choose a recorder. All comments are written down. This means that each person’s contribution is captured and made visual for the table. Do not simply write these on a computer. Sometimes the person who made the comment will want to revise this, or expand on it. Sometimes the recorder will not understand the comment, and will ask for clarification. Everyone’s voice is heard in this process. The conversation moves as fast as people can talk. When silence breaks out, the facilitator will come by to ask if the table needs another question.

RULE 7: Create narratives from the Post-its and put these online immediately

Have a volunteer at each table who merges the contents of the big Post-its into a narrative. This narrative might be one paragraph, or several. You can capture these narratives in any way that works for you. Google documents, shared Dropbox: whatever you are most familiar with. The Post-its and these narratives are the output from the meeting. They are the gold you have woven from the ideas of your participants. It is tempting to skip the Post-it and go right to a computer. Do not allow this. The Post-it step is there to keep the conversation flowing and the let each person know their ideas are being captured.

RULE 8: Many conversations in one room

Workshop planners often make the mistake of having a plenary room and then breakouts in separate rooms. Set up the main room in round tables; all the conversations will happen there. It will get loud, but people will also gain energy from the buzz in the room. And when they are voting with their feet, they only need to meander to another table and join the conversation.

RULE 9: No long introductions. No formal report-outs, but quick checks. No breaks, break anytime

At the start, have each person stand and say their name. Then have them give three words that express their hopes for the day. This should take only 10 minutes.  During the course of the day, have a volunteer print out the narratives from the tables and post them on the wall near the coffee in real time. Let the display of these become an ongoing marker of the accomplishments of the workshop.

Do not have any special report-out breaks, this only slows down the conversation. Do not schedule coffee or other breaks (except for lunch if you started in the morning), but encourage everyone to take a break any time they feel like it. They can get coffee, walk around the block, and do whatever they need to gather their attention back to the workshop. Once an hour, the facilitator will do a quick check-in with the room. Stop the conversations briefly to ask if there are any concerns about the process, and remind people to go look at the report-out wall. Arrange a good lunch for everyone.

RULE 10: Facilitator keeps the conversation going

The Santa Barbara Workshop is a fast-moving symphony of conversations and inspirations. The key is to keep the ideas flowing, capture these as effectively as possible, and support each table with a supply of questions and a recording mechanism. The main facilitator will walk among the tables ready to supply a new question, or to gather the “hot topic” questions for other tables to answer. The facilitator will also decide when to rotate the tables, and can help keep the process on track.

At the end of the day, be prepared for the participants to be excited and exhausted. They will feel like their ideas have been heard, and their contributions have been saved. When they browse the report-out display, they will see how their table’s answer to the questions exposed different solutions from those of other tables. They may want to be alone after eight hours of constant conversation. They might be ready for some beer.

At the end of the day, you will likely have a document that is hundreds of pages long, with multiple insights into the key questions that your organization faces. You will have mined the best ideas from 35 people. And these 35 people will leave the workshop satisfied that their time and their expertise has been well used and honored.

 My brain hurts

The Santa Barbara workshop can be used for a wide range of planning and design problems. You can do “mini-workshops” with two or three tables. There are other similar styles of workshop, you can look them up on the web. When you ask anyone who has participated in this type of workshop, they will tell you how much fun they had getting their brains picked clean. They might also note that other workshops, where they are forced to watch PPTs in a room with 100 others, and then raise their hands one-by-one to speak, now seem boring and inefficient. This is the downside of the Santa Barbara Workshop: once you’ve gone there, you can never go back.

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