A logic of abundance

Get Ready for Abundance

“The opposite of scarcity is not abundance. The opposite of scarcity is… enough” Brené Brown Podcast <https://youtu.be/YxAcUOelnAY>.

“Scarcity is easier to deal with than abundance, because when something becomes rare, we simply think it more valuable than it was before, a conceptually easy change. Abundance is different: its advent means we can start treating previously valuable things as if they were cheap enough to waste, which is to say cheap enough to experiment with. Because abundance can remove the trade-offs we’re used to, it can be disorienting to the people who’ve grown up with scarcity. When a resource is scarce, the people who manage it often regard it as valuable in itself, without stopping to consider how much of the value is tied to its scarcity” (Shirky 2010).

“For the Ju/’hoansi, that fundamental axiom of modern economics, “the problem of scarcity”, simply did not apply. Where this holds that it is human nature to have infinite wants and limited means, the Ju/’hoansi had few wants that were simply met” (Suzman 2017 [The Guardian]).

Abundances

We have not really begun to explore the many varieties of abundance that can emerge once we abandon arbitrary scarcity. It is important to remember, as Brené Brown (2012) reminds us, that abundance is also simply enough: enough access and discovery tools to know your research is new and where it sockets into existing knowledge; enough communication with colleagues working in your specific arena to collaborate your way through pain points; enough research funding to move ahead; enough time to do your work, and; enough credit to know your work is out there and appreciated. Enough is never too much. It is all the abundance an open scientist needs.

Primary abundance is built into digital science objects which, like Jefferson’s thoughts, can be copied infinitely without diminishing the original. Quite the opposite, the more copies that circulate, the more valuable the original object becomes, only not as the private property of an individual, but rather as a common pool resource for the commons.

Combinatory abundance is what happens when science objects (and scientists) enter into collaborative matrix to mix, meld, and produce new objects. This is also where the network effect applies to objects, not just people.

“The difference between humans and animals lies in the ability to collaborate, engage in business, let ideas, pardon the expression, copulate. Collaboration has explosive upside, what is mathematically called a superadditive function, i.e., one plus one equals more than two, and one plus one plus one equals much, much more than three. That is pure nonlinearity with explosive benefits—we will get into details on how it benefits from the philosopher’s stone.” (Taleb 2012) paraphrasing Ridley (2010).

Language is a good example of the kind of combinatory abundance that open science hopes to achieve through mineable/mixable repositories of a wide variety of knowledge objects. The English alphabet has twenty-six letters and the English language about forty phonemes. From these all words, sentences, paragraphs, texts, and conversations are spun by combining and assembling them using rules and shared semantics.

You’re an academic, you know that academics might run out of patience, or time, or even wine, but rarely do we run out of words (or ideas for that matter). In fact this is one abundance that we have always enjoyed, perhaps a bit too much. To achieve the “explosive upside” of collaboration, scientists need to build open cultures of collaboration.

Emergent abundance describes the complex objects of study, the unknowns that feed science and also science’s willingness to not seek “truth”. Whether you are tracking the micro-second changes of a single cell or the collision courses of galaxies, you begin with a never-decreasing abundance of questions. Science also has an abundance of doubts, as well as discoveries. Science swims in an ocean (an abundance also) of doubt, as Richard Feynman reminds us: “A scientist is never certain. We all know that. We know that all our statements are approximate statements with different degrees of certainty; that when a statement is made, the question is not whether it is true or false but rather how likely it is to be true or false” (Feynman 2005).

What emerges from these doubts is a collective form of being only slightly less…wrong. Being less wrong iterates into being somewhat more right, but never to the point of actual truth. Everything we know today will be different from what we know tomorrow.

“[S]cientists gravitate toward falsification; as a community if not as individuals, they seek to disprove their beliefs. Thus, the defining feature of a hypothesis is that it has the potential to be proven wrong (which is why it must be both testable and tested), and the defining feature of a theory is that it hasn’t been proven wrong yet. But the important part is that it can be — no matter how much evidence appears to confirm it, no matter how many experts endorse it, no matter how much popular support it enjoys. In fact, not only can any given theory be proven wrong; … sooner or later, it probably will be. And when it is, the occasion will mark the success of science, not its failure” (Schultz 2011).

Infinite abundance marks the recognition that science is not a finite game. There is no way to “win” science; no ending of science; and no possibility for its rules to be fully known; these are continually subject to change. The great mistake of bringing the logic of the marketplace (a finite, zero-sum game) into the academy is that it promotes behaviors that treat science like a finite game, and it makes competitors out of colleagues.

As a form of infinite play, science finds itself in a never-ending tussle with its objects of study; “Our freedom in relation to nature is not the freedom to change nature; it is not the possession of power over natural phenomena. It is the freedom to change ourselves. We are perfectly free to design a culture that will turn on the awareness that vitality cannot be given but only found, that the given patterns of spontaneity in nature are not only to be respected, but to be celebrated” (Carse 1987). James Carse’s book, Finite and Infinite Games, offers a great heuristic for the type of culture change needed for science to become “open science”

“THERE ARE at least two kinds of games. One could be called finite, the other infinite. A finite game is played for the purpose of winning, an infinite game for the purpose of continuing the play.”
…
...“It is on this point that we find the most critical distinction between finite and infinite play: The rules of an infinite game must change in the course of play. The rules are changed when the players of an infinite game agree that the play is imperiled by a finite outcome—that is, by the victory of some players and the defeat of others. The rules of an infinite game are changed to prevent anyone from winning the game and to bring as many persons as possible into the play.” (Carse 2011)

Sufficient abundance reminds us that abundance does not need to be a waterfall into an overflowing bucket. As long as the bucket is full, there is abundance. A single extra drop makes it overflow. Abundance is relative to needs, and needs can be managed to the level of sufficiency, rather than expanded by market-fueled desires. In reality, abundance just means this: enough.

Open science advocates are often asked about how they will replace (perverse) market incentives; as if these are the only incentives out there. Scientists have their own incentives, the reasons they are scientists and not, say, hedge fund managers. And scientists were fully incentivized in the decades before the marketplace intruded on the academy. There are many articles about the mismatch between science and market incentives. A good place to start is Edwards and Roy (2016):

“In this article, we will (1) describe how perverse incentives and hypercompetition are altering academic behavior of researchers and universities, reducing scientific progress and increasing unethical actions, (2) propose a conceptual model that describes how emphasis on quantity versus quality can adversely affect true scientific progress, (3) consider ramifications of this environment on the next generation of Science, Technology, Engineering and Mathematics (STEM) researchers, public perception, and the future of science itself, and finally, (4) offer recommendations that could help our scientific institutions increase productivity and maintain public trust. We hope to begin a conversation among all stakeholders who acknowledge perverse incentives throughout academia, consider changes to increase scientific progress, and uphold ‘‘high ethical standards’’ in the profession…”

Offer a scientist more time, cheaper tools, and some security to finish their research, and you will have a happy scientist. Chasing reputation points and writing endless proposals for funding would not compete with simply clearing the decks and letting research come to the fore. Managing needs can be a productive alternative to bulking up the CV with marginal publications. Open science can wean the scientist from perverse incentives by offering more with less.

Are you tired of working so hard to get just a bit more? One of the tasks of open science is to innovate to lower the costs of doing science. The most “successful” societies in the history of humanity became affluent by managing their needs:

“[Marshall] Sahlins characterized hunter-gatherers as the gurus of a “Zen road to affluence” through which they were able to enjoy “unparalleled material plenty— with a low standard of living.” Here, it seemed, was a people unconcerned with material wealth, living in harmony with their natural environments, who were also egalitarian, uncomplicated, and fundamentally free” (Suzman 2017).

Sometimes one can achieve abundance by simply finding a smaller bucket. Matching needs to resources (instead of the other way) can tip your situation from scarcity to “just enough”.

The next steady-state of science is the provident abundance of resources

After decades of scarcity, real and imaginary, and of regimes of funding that encouraged hyper-competition, the move to a research eco-system of abundance will necessitate new practical knowledge to navigate and optimize this. At some point, scientists will become comfortable knowing that they can find and demand what they need to do their work. They will become affluent academics: rich in the resources they require, even as they are detached from marketplace incentives. Like hunter-gatherers, their ability to glean what they need means they no long have any reason to hoard what they have.

“In part, the Ju/’hoansi’s affluence was based on their unyielding confidence in the providence of their environments and their skills at exploiting this. Ju/’hoansi still make use of well over 150 different plant species, and have the knowledge to hunt and trap pretty much any animal they choose to. As a result, they only ever worked to meet their immediate needs, did not store surpluses, and never harvested more than they could eat in the short term” (Suzman 2017 [The Guardian]).

Building provident academic resources (repositories, metadata, platforms, etc.) for reusable digital objects, global in scale but also localizable for use, is a major task for open science. The enormity of this task has occupied a lot of attention in the past decade. National and international data organizations, and professional associations and universities (with important nudges from funders) are working on parts of this task.

There is an equally important task that open scientists need to tackle: Governing these resources and forging the shared social practices that can do so. This second task represents a major cultural challenge for the open science academy, and for each part of this (academic departments, research labs, professional associations, universities, funding agencies); and, finally, for each individual open scientist. Culture is aways shared, and always carried by individuals.

Scarcity, as we will see next, is mainly manufactured to game values in the marketplace. If you make less of something, when the demand is constant, you an increase the price.

Final thought: Abundance is how you perceive your work.

“If you perceive the universe as being a universe of abundance then it will be. If you think of the universe as one of scarcity, then it will be. … I always thought that there was enough of everything to go around—that there are enough ideas in the universe and enough nourishment.” Milton Glaser in (Millman 2007).

Scarcity is mostly manufactured and arbitrary

“I am often told that I should be grateful for the progress that Western civilization has brought to these shores. I am not. This life of work-or-die is not an improvement on preinvasion living, which involved only a few hours of work a day for shelter and sustenance, performing tasks that people do now for leisure activities on their yearly vacations: fishing, collecting plants, hunting, camping, and so forth. The rest of the day was for fun, strengthening relationships, ritual and ceremony, cultural expression, intellectual pursuits, and the expert crafting of exceptional objects. I know this is true because I have lived like this, even in this era when the land is only a pale shadow of the abundance that once was. We have been lied to about the ‘harsh survival’ lifestyles of the past. There was nothing harsh about it. If it was so harsh—such a brutish, menial struggle for existence—then we would not have evolved to become the delicate, intelligent creatures that we are” (Yunkaporta 2020).

“As Lawrence Lessig has so persuasively argued over the years, there is nothing ‘natural’ about the artificial scarcity of intellectual property law. Those laws are deliberate interventions crafted by human intelligence and are enforced almost entirely by non-market powers. Jefferson’s point, in his letter to McPherson, is that if you really want to get into a debate about which system is more ‘natural,’ then the free flow of ideas is always going to trump the artificial scarcity of patents. Ideas are intrinsically copyable in the way that food and fuel are not. You have to build dams to keep ideas from flowing” (Johnson 2011).

Ideas are not like food, nor fuel. But then the scarcity of jobs, careers, and funding is not simply artificial either. Scarcity is real, you might say. However real this scarcity is today, it is not necessarily as durable as you think. So, how does open science fuel a new abundance for these various resources?

Let’s start with the problem. Scarcity is created to build markets where arbitrary values can be maintained to guarantee a profit. Widlock describes it this way: “Modernity defines itself as a ‘culture of scarcity’…, of there never being enough goods and information. The assumed ‘eternal shortage’ paradoxically seems to grow, rather than to diminish, as modernity and capitalism unfold and ever more consumer needs are created. The underlying orthodox assumption is that economy is above all economizing (making ends meet) and directed toward utility as being conditioned by limited means employed to satisfy unlimited wants.” (2016) Of course, the “wants” are also manipulated by the marketplace: they are attached to the outcomes of the scarcity machine.

Bauwens (2012) adds that markets cannot survive without scarcity: “Markets are defined as ways to allocate scarce resources, and capitalism is not just a scarcity ‘allocation’ system, but, in reality, is a scarcity engineering system, which can only accumulate capital by constantly reproducing and expanding conditions of scarcity. When there is no tension between supply and demand, there can be no market, and so no capital accumulation” <http://realitysandwich.com/142773/evolving_partner_state_ethical_economy/> Accessed 10/2018). Markets only succeed when they can manufacture enough scarcity to raise the price of what they manufacture high enough to make a profit; they have the means to make enough of the goods to drive the price down, but chose not to do so (Siefkes 2008).

The market logic of scarcity, and the resulting neoliberal push to remake the academy as a business venture has been a uniform disaster for universities, and an existential threat to the reputation of science. Science findings that are infested with conflicts on interest create doubt in the public. When scientists are suspected of not being truthful, of not upholding the primary sincerity required for the scientific method to stand, then their findings, even when these might be true (to the amount this is available), will be met with doubt. This impacts the ability of the public to rely on science for their own sense-making, and undermines science’s position in the public information ecology (See: The War on Sense-Making; Accessed 10/31/2020).

Open science alone cannot reverse this trend. Expecting open science to fix the academy without first extracting this from a hyper-rivalrous economy puts too much on the back of this cultural shift. But the practices of open science can offer expanded returns on the investments the state makes into the academy. These practices optimize the value of research objects and reground science by its internal norms. The culture of open science can help return the academy to a value proposition that centers research and teaching as a public good that reaches toward social justice and broadly-felt benefits across society.

Open science values contributions from a broader cohort or intellectuals. Doctorate holders represent a tiny proportion of the planet’s population (less than 2%). But the problems the world faces are more complex and numerous than ever before. “There is, however, no limit on society’s need to address complex challenges, the number of research questions that can be asked, or the amount of scientific work that can be done. New models are needed to help identify different ways for scientists to continue their work outside of a standard academic or agency job” (Lancaster, et al. 2018).

Right now is the time to euthanize Matthew

Open science culture prioritizes widespread, global participation, which would necessitate redistributions of academic fortunes away from current elite organizations and funder-favorite research endeavors: the result of decades of cumulative advantages, sometimes called the “Matthew Effect.” The process of “un-accumulating” advantages in the academy will be painful, particularly to those who have invested in them.

Today, “[d]ifferences in individual capabilities aside, then, processes of cumulative advantage and disadvantage accentuate inequalities in science and learning: inequalities of peer recognition, inequalities of access to resources, and inequalities of scientific productivity” (Merton 1988). Tomorrow, an open-science academy will need to refactor decision processes to spread around resources that are currently limited, and challenge the current level of available funding by making the case that it can optimize the return on the value—including a wide range of social benefits—for public investment in the public goods of science.

“The ecosystem metaphor is our attempt to reframe and expand the discussion of STEM careers and science beyond what has often become a sterile and arid debate about competition and scarcity within academia by connecting it with the open flows, resilence, diversity and feed-back loops of ecological systems. …[W]e strongly believe that making science better is not just ‘making the incentives better’, but a collective cultural shift beyond viewing competition and individualistic success as the sole defining feature of science (i.e., the pipeline model)” (Lancaster et al. 2018).

Instead of an economy that relies on hyper-competition to create an underclass of PhDs (Chapman, C.A., et al. 2019) for hire at almost any price, an open, sharing academy economy would find guaranteed employment for all PhDs at one of the many available academy institutions, or elsewhere in society (See: Tcherneva (2020) on the benefits of job guarantees).

Idea Farming

Open science means leaving the idea desert for the idea garden

Most often, new discoveries and new learning come when one is open to serendipity, when one welcomes novelties and anomalies, and then tries to incorporate those outlying results into the broader field of knowledge. As Isaac Asimov said, ‘The most exciting phrase to hear in science, the one that heralds new discoveries, is not “Eureka!” but “That's funny”’ (Brown 2009).

Ideas in the academy are another victim of the logic of arbitrary scarcity. They are also collateral damage in the proximity to the neoliberal, start-up economy. The academy should be an idea hot-house, instead we have an idea desert. The cultural shift to open science will be final when ideas flow across the globe like pinot at a faculty party.

Common sense on idea sharing

Like talk, ideas are cheap. How many research ideas do you have in an hour? In a day? In a week? Probably enough that you really wouldn’t take time to even jot them down. There are the ideas that seek to connect your current work to new hypotheses. Ideas about alternative methods, or new data sources. So many ideas crowd into your thoughts:

• Ideas you have while listening to a seminar talk outside your field, where you are curious how something you know might be of use or interest;

• Ideas on where your discipline is headed, and those large-scale issues that might drive agency agendas;

• Ideas that pop up when you read that new journal article (any article that does not give you new ideas is a waste of your time);

• Ideas about what your graduate students might want to pursue to start their own infinite play in science;

• Ideas you put into that NSF proposal you submitted last month;

• Ideas…. Well; you get the idea…

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Face it: your professional life is brimming with ideas; that’s pretty much the point. And yes, you don’t want or need to share them all. Over time, you will get better at triaging the insights that occur to you. Ideas are the starting line for infinite play (See: Learning infinite science play). There are about ten million science researchers on the planet. Each of you wakes up to a new day filled with new ideas. Almost all of you keep most of your ideas in your head until they are forgotten, replaced with other ideas, similarly forgotten, and a couple insights, carefully hidden in a notebook or on a laptop. We live in a world where there is no lack of abundance of good ideas in science. We also live in a world with a global internet. Why not connect these two? That’s an idea.

Note: thoughts are not ideas. Thoughts are just thoughts; you have a steady stream of these, most of which you would not share without first being injected with Sodium thiopental. Also, nobody wants to hear your thoughts.

When asking scholars about their ideas, some common notions arise:

1.) Even though that idea you just had while brushing your teeth might seem unremarkable to your own work and aims, the only way for this to engage the adjacent possible of someone else’s research is for you to share it. Some time in the near future, you will be able to pick up your phone and speak your idea into an online service that can push this into a global science conversation. Then you can rinse the toothpaste from your mouth. “(W)e can be sure that accidents will continue to happen and, with human minds better prepared than ever before, we can expect these accidents to be turned into discoveries, marvelous beyond our imagination, through serendipity” (Roberts 1989).

Great ideas can happen any time, anywhere. And none of them were recognized as “great” at the time. Histories of science record numerous events where ideas that informed major scientific breakthroughs began as simple thoughts that occurred on buses, in showers, while walking or waking, and, most regularly, during informal conversations away from the laboratory (See: Copeland 2019; Roberts 1989). Discoveries made by mistake are commonly referred to as occasions of scientific serendipity: looking for X in the lab, and finding Y instead. “Serendipity is built out of happy accidents, to be sure, but what makes them happy is the fact that the discovery you’ve made is meaningful to you. It completes a hunch, or opens up a door in the adjacent possible that you had overlooked” (Johnson 2011). Yaqub (2018) outlines several different types of scientific serendipity that your next idea might trigger.

2.) Almost all of ideas you produce throughout the day, you dismiss as worthless. The few you write down to remember you cherish as diamonds. And yet, while the former are of little value to you at this moment, many of the ideas you have might be insights of some significance to another scholar. These ideas are products of your own genius, created without effort, because of the enormous effort you’ve already made through your learning. Don’t get proud here, there are millions of researchers with similar talents, each one focussed on their own research problems. Each one’s personal variety of sagacity makes their ideas different; and this difference is the key to the new information they hold for others.

What about those ideas you cherish? Those precious nuggets you hoard like Smaug in his cave? Give them away. It’s a practice of the open-science Demand Sharing economy. You let others have what is most valuable to you. This generosity encourages them to do likewise. When the smartest person in the room is the room, filling this room with the best ideas opens up floodgates to creativity and innovation. The more precious your ideas are to you, the more likely they are worth being borrowed by others. When you add these to the conversation, you are also announcing your authorship of them. They are your gift to the academy that supports your own research. Others might add new insights to your idea enlarging and expanding its value. Only it’s not yours any more, it’s a part of an idea commons. And don’t worry. You’ll have plenty of new ideas to give away.

3.) Your idea needs friends to get great. An idea on its own in your head is like a seed in a seed packet. It needs ground to grow, it needs to join into conversations. Until now, these conversations happened in your lab or at a workshop. “(M)ost important ideas emerged during regular lab meetings, where a dozen or so researchers would gather and informally present and discuss their latest work…. the ground zero of innovation was not the microscope. It was the conference table” (Johnson 2011). Today, those conversations can and, perhaps, must happen at a much larger, online, table (See: Science happens elsewhere).

You may be mistaken about science idea sharing in your career

1. You think it’s a self-defeating move to openly share your ideas. However, you already can share almost all your important ideas with a win-win outcome; and,

2. Funding agencies are the least efficient organizations when it comes to gathering important ideas. That RFI you just filled out is a good example.

3. Idea farming is a fringe notion, you think. It turns out that hundreds of corporations and public government organizations are actively doing this right now.

These misconceptions will also change when a culture of demand-sharing open-science is implemented across the academy. Let’s explore further.

You are ready to share your ideas anyhow

Right now, today, a lot of the ideas you have that are somewhat relevant to your work, you would share if this were easy enough to do. You have any number of potential solutions for a wide range of issues in your area of research; solutions you have no intention of pursuing, but would really like to have solved, by someone, and today, if possible. For you, these are anti-rivalrous ideas. You don’t mind if someone else, or anyone else, takes them to work on. Guess what, your idea might be a catalyst for someone else’s research; just the idea that leads them to a breakthrough that will make your work easier tomorrow. If there were an idea farm on the web, you could certainly spend ten minutes a day contributing smart ideas for others to work on. At the same time, you can lard the idea farm with questions you need answers to, and pull these from the mix when they show up. The small remainder of your ideas are those you and your team might want to propose to accomplish, given funding. So you tuck these away. And even when your research proposal is being evaluated, you worry that someone in that process will grab them for their own proposal (after down-grading your proposal); such is the state of the academy today. Open science sharing would allow you to give away all your ideas, and still get recognition for them. The problem isn’t greed, it’s the culture of the academy that needs to change.

The RFI paradox: scientists offer information that seeds their research

Every so often a funding agency/foundation asks for feedback: they want your ideas about priorities for the discipline’s future research. Ideally they would get a vast range of information from the thousands of scientists on their mailing lists. But realistically, they only get granules of ideas that are linked tightly to the goals of the teams/labs that will be angling for funding. “What should we focus on?” they ask. “Me,” you answer. Not so directly, but by the content you supply.

Idea-gathering by funders is perhaps the least effective way to assemble knowledge about science. One major private foundation recently discovered, when it opened up an idea-farming platform to gather ideas that almost every idea came with a request for funding. This is not the fault of the researchers. They have five-hundred words to say what it most important for their discipline. What is most important for their discipline, in their perspective, is to support an arena of research in which the researcher has already invested.

An open platform for your ideas to procreate

What if every day, say at the end of the work day, or after a beer, or in the morning after that mug of coffee, each scientist on the planet hopped online and added one idea to the global idea-farm platform (with some tags to help discovery)? What if ten-percent of them decided to add lots of ideas every month (the power-law curve suggests this is inevitable)? After a single year, there would be more than three billion ideas on the platform. Lots of overlap and similarities, but a whole lot of variety and difference too; coming from the minds of people who woke up in a hundred different nations. Each idea is time-stamped, with a permanent ID, and linked to its author. Every entry takes a minute or two to accomplish. A phone app lets you talk your idea into the mix.

Want to add a crazy good idea, or worried an idea might seem naive? Use your personal alias. Want to add a comment or a question to someone else’s idea? Go ahead. Feeling paranoid? Lock your proposal insight into an embargoed, timestamped vault on the platform. Open this later. Then try to be less paranoid.

Demand sharing means giving what is most valuable to you to the academy. This is a value and a norm for open science. Open science initiatives are building open platforms for a variety of internet services. The platform for open idea farming may not be here now, but can be built with a bit of funding and the right home.

Link a billion ideas to a million scientists across the planet, and you can find the select few of them who happen to be considering precisely the same problematic you are puzzling through today. Then you can build collaboratives to explore these together. Thinking of writing a grant proposal? Mine the combined idea farm of the planet to make your proposal ideas better and more up-to-date; and then share these new ideas online (you can embargo them if you are worried). Your graduate students will be looking to see where their ideas are shared elsewhere, and how they can push their own infinite play into new ground. You can mine the platform to sharpen your paper or your poster. What better way to learn new things when you’ve already finished school, than to access the ideas of your peers? Network effects not only apply to people, but also to ideas. Put a lot of ideas into a shared, networked (databased, searchable, with discovery tools) environment, and innovation will blossom. This environment will become a place where, as Matt Ridley says, “ideas go to have sex.”

Become an ImagiNative

What if one of your ideas (you had this in the shower, and spoke it into your phone app over coffee) were picked up by a lab in another county, on another continent, and used to create a new theory that rocked your discipline; and in the paper that announced this theory, your idea was cited as a key element? How rewarding would that be? How many times might this happen across the planet in an open-innovation environment? And what if you searched the platform and found an idea from an early-career scientist in Sri Lanka that gave you a new insight into your current work, so you cited them in your next paper. How great for them.

There is a whole lot of “elsewhere” out there in the global Republic of Science. You need to be in touch will all these elsewhere ideas and with the people thinking them who also share your disciplinary/theoretical neighborhood. As Shirky noted, “We also have to account for opportunity, ways of actually taking advantage of our ability to participate in concert where we previously consumed alone” (2010). You need to become an ImagiNative; open to new modes of collective knowing. And your lab, your school, your university needs to support open innovation (instead of patents; see: Against Patents in the Academy).

“Innovation happens everywhere, but there is simply more elsewhere than here. Silly as it sounds, this is the brutal truth: Regardless of how smart, creative, and innovative you believe your organization is, there are more smart, creative, and innovative people outside your organization than inside” (Goldman and Gabriel 2005).

One of the major changes for corporate R&D in the past twenty years is “open innovation” (Johnson 2011). This has become a clarion call for the academy too (Europäische Kommission 2016). Sharing ideas and insights on an open platform transforms the various elsewheres of the academy into new opportunities for open innovation. Dozens of “innovation management” platforms today help global corporations mine the ideas of their wide-spread workforce and their customers.

Open Scientists share objects to gain network effects for their work

“Because we have to coordinate with one another to get anything out of our shared free time and talents, using cognitive surplus isn’t just about accumulating individual preferences. The culture of the various groups of users matters enormously for what they expect of one another and how they work together. The culture in turn will determine how much of the value that we get out of the cognitive surplus will be merely communal (enjoyed by the participants, but not of much use for society at large) and how much of it will be civic.” (Shirky 2010)

Building on a civic culture of sharing, open science creates new value from every object (idea, data, method, software, results) that is openly shared. Some of this new value accrues to the scientist who shares, some goes to the benefit of all scientists working in the same research arena who reuse this object, and some goes to scientists who can open up new research from the collective resource that this object now enhances. This last value is the ultimate promise of open science: a shared surplus of research objects the can be openly mixed, mined, and melded into new, synthetic knowledge. McKiernan (et al. 2016), demonstrates the advantages of open sharing for citations, impacts, careers, etc. What the open scientist does to increase the holdings of the open corpus in their field adds a civic choice to these advantages. Growing the open research ecosystem helps every scientist on the planet.

Adding a new bit of research findings and process to an open repository is as easy (or easier) than submitting this to a closed collection (such as a for-profit publisher). However, open sharing scales better, particularly when it uses open standards-based platforms, and it is less fragile, as it can be migrated to new platforms and spread across multiple locations. Openness adds to discoverability and access, and contributes to reproducibility.

Even as the value of, say, a telephone exchange, increases with each new telephone connection, the addition of a new data set, or a null result paper, or a specific finding builds numerous interconnections with the rest of the corpus. These interconnections (and their “network effects”) can lead to new knowledge, and they can serve as a mirror and a measure to reveal how each new bit of content solves (or critiques) a specific issue, and also potential problems with the newly added object. Rapid, open review opportunities arise. So too does rapid recognition and opportunities for new collaborations.

A lot of these new interconnections will take place on the internet at a planetary scale. The network effects of open science build capacity for the free movement of objects and ideas. This capacity—the almost instant global access to science products on the open web—is anathema to markets that need to claim ownership and restrict access in order to capture profits from these. Distributed data protocols (e.g., the Interplanetary File System) and other emergent technologies will reduce the cost of hosting science objects to a near zero margin. Open licenses make sharing science knowledge durable and its reuse legal.

As Cameron Neylon said at the metrics breakout of the Beyond the PDF conference some years ago, reuse is THE metric. Reuse reveals and confirms the advantage that open sharing has over current, market-based, practices. Reuse validates the work of the scientist who contributed to the research ecosystem. Reuse captures more of the inherent value of the original discovery and accelerates knowledge growth. Open science is a science knowledge and data reuse accelerator. Its network effects help make reuse available, and, in time, inevitable.

Please Stop Getting Better at Obsolete Science Practices

Against Patents in the Academy

It’s time to eliminate patents in universities: Step up to Open

“It is true that many people in science will scoff if you try to tell them about a scientific community in which ideas are treated as gifts. This has not been their experience at all. They will tell you a story about a stolen idea. So-and-so invented the famous such and such, but the man down the hall hurried out and got the patent. Or so-and-so used to discuss his research with his lab partner but then the sneaky fellow went and published the ideas without giving proper credit. He did it because he’s competitive, they say, because he needed to secure his degree, because he had to publish to get tenure — and all of this is to be expected of departmentalized science in capitalist universities dominated by contractual research for industry and the military” (Hyde 2009).

In researching the forty years of allowing publicly funded primary research results to be patented in the US, what becomes clear is that for every success story there are scores of negative outcomes. The bureaucracy that universities build to capture the “value” of research as patents (Welpe et al. 2015), the administrative burden on researchers to conform their work to the process of patent-making (Stodden 2014; Graeber 2019), the perverse career pressure to produce more patents (Edwards and Roy 2017), the downstream roadblocks for sharing the research (NAS 2018): the entire ecosystem (or egosystem) of doing patents argues against their benefits to the academy. The underlying tension between the university’s long-term mission as a wellspring of new public knowledge and the market’s desire to acquire and privatize new discoveries remains at issue here (Foray and Lissoni 2010).

The Handbook is not a primary source for arguments around patents, and will only point to some major issues and a handful of available resources on this topic. Gerald Barnett (Accessed August 31, 2020) has assembled a useful resource on the web for those interested in university patent issues. The Handbook argues that open science works best within a shared resource commons, where the neoliberal market is held apart, but likely never fully absent, for the duration. Open science and closed knowledge transfer practices do not play well together.

You might work in one of very few select sub-disciplines (usually within bio-medical or IT research) at one of the few universities where university patents have had some historical financial returns. But for the other ninety-five percent of science, and for the academy as a whole, the value of sharing research far exceeds whatever near-term monetized return might be available. Newfield (2016) summarizes the situation this way:

“The point here is not that the University of California and American research were doing badly. To the contrary, they were producing the normal market results of doing research very well, which (with rare exceptions) is to spend lots of money rather than to earn it. The market results of innovative research are, as research results, close to nil. This is as it should be. The purpose of innovative research is innovation — discovery, invention, and scientific progress. This research has great long-term and social value that could not be captured as licensing revenue or estimates of the market value of patents.

The contribution of the research university can best be appreciated in broader, postmarket terms. The research university was designed to investigate every topic of conceivable public interest, from astronomical physics to agricultural genetics and everything in between. Major commercial returns accrued to research in a fairly narrow band of fields largely found in information technology and biomedicine….”

University research results have also, historically, been “transferred” to the academy, industry, and the public through a diverse portfolio of channels (publications, workshops, conferences, etc.). Patents interfere with these other channels. “[W]idespread patenting and restrictive licensing terms may in some cases hamper, rather than promote, technology transfer from universities to industry. These policies may also obstruct the process of scientific research (Mowery et al. 2001). Foray (2004) puts it like this: “Most studies on these issues show that this evolution [toward patenting basic research] represents a real risk of irremediable alteration of modes of cooperation and sharing of knowledge in the domain of basic research. When there is nothing left but exclusive bilateral contracts between university laboratories and firms, there are forms of quasi-integration that undermine the domain of open knowledge.”

Remembering here that science is infinite play (See: Learning infinite science play). The actual returns on research are mostly “postmarket” in value. Open sharing accelerates returns in the near term and compounds research value over time. Universities achieve their value proposition through a broad range of research and educational activities. The availability of market returns from patents for a small segment of university research threatens to warp the research opportunity landscape, and the normative internal incentives (including curiosity) for research (Strandberg 2005).

“In an age when ideas are central to the economy, universities will inevitably play a role in fostering growth. But should we allow commercial forces to determine the university’s educational mission and academic ideals? In higher education today corporations not only sponsor a growing amount of research — they frequently dictate the terms under which it is conducted. Professors, their image as unbiased truth-seekers notwithstanding, often own stock in the companies that fund their work. And universities themselves are exhibiting a markedly more commercial bent. Most now operate technology-licensing offices to manage their patent portfolios, often guarding their intellectual property as aggressively as any business would. Schools with limited budgets are pouring money into commercially oriented fields of research, while downsizing humanities departments and curbing expenditures on teaching” (Press and Washburn 2000; Accessed August 25, 2020).

Time to act

Open science looks ahead to a future where the capacity to share research findings is optimized through scholarly commons, collaboratives that steward research goods through the decades, and across the planet (See: scholarly commons; Also, Madison et al. 2009). Patents subtract intellectual property and value from these commons: “[T]o the extent that universities surround the work of their scientists with thickets of patents, the upshot can be what Heller and Eisenberg [1998] call a scientific ‘anticommons’ in which ideas and concepts that in the public domain might spur discovery and innovation are zealously guarded by the institutional owners who value income more than innovation” (Ginsberg 2011). Researchers may also shy away from research arenas where existing patents impede new research (Foray and Lissoni 2010).

Looking ahead, the rapid increase of mostly under-performing (in terms of financial gain) patents creates no-research zones across formerly attractive knowledge domains. This growing patent infestation — intellectual property kudzu clogging the shared open resource pool — may be an unfortunate near-future end game for university patents, strangling new research. But a better plan is to clear away these anticommons today.

In the US, the repeal of Bayh-Dole — the act that permitted universities to patent federally-funded research — would open up old (and now, new), long-term research sharing capacities (Barnett, May 10, 2020; Accessed August 26, 2020). Putting the market-incentive genie back in its bottle will help universities shrink their administrative overhead, help researchers manage their own research interests, and help the academy get on with the real business of science: its mission to openly share knowledge within an abundant gift economy in order to foster new discoveries of benefit to all humans. However, Bayh-Dole is only one of a couple dozen post-WWII US laws that regulate and channel intellectual property flows among universities, government labs, and industry (Slaughter and Rhoades 2010). These laws were created to knit university research outputs into the surrounding neoliberal marketplace. Each of these laws needs to be reassessed for its impact on the other knowledge dissemination flows universities have long used, and on the long-term mission of academic organizations. As the U.S. Code is a maze of regulations that are stacked on previous laws, simply repealing one of these (such as Bayh-Dole) is rarely a good fix. Its removal simply exposes the problems created by the previous laws (Barnett, August 31, 2020; Accessed September 1, 2020).

There are two options around Bayh-Dole. The first would be a new national law that revokes and replaces large parts of Bayh-Dole without repealing it; a kind of Bayh-Dole antidote that neutralizes the previous law and adds another wart on the dimpled surface of the U.S. Code. The problem here, as John Wilbanks (personal communication) surmises: universities would likely find contractual means to work around the new law and keep doing what they do in a somewhat weaker mode. A university culture of neoliberal, short-term gain will find a way to circumvent the new law. The second option is more pervasive and effective over time: change university culture to neutralize Bayh-Dole. “Any university could in effect repeal Bayh-Dole by creating an open scholarship favorable patent policy. Claim nothing up front. Require no disclosure of inventions” (Barnett, ibid). Here is a concrete cultural change that open scientists can take to their faculty senates and board of regents. Barnett (ibid) spells it out with some precision:

“Thus, the shortest route to open is to insist that universities comply with the extraneous requirement of the nonprofit standard patent rights clause at 37 CFR 401.14(f)(2)—require the written agreement, making inventors parties to each funding agreement, and declining to take any interest in any invention the inventors might make under the funding agreement (which in turn brings the university into compliance with the extraneous requirement at 37 CFR 401.14(g)(1)). With compliant (f)(2) agreements in place, inventors have no obligation to disclose subject inventions to the university or to the federal government so long as the inventors do not make the inventions know[n] to the inventors’ patent personnel and the university does not claim ownership of the inventions and require the inventors to make the inventions known to the university’s patent personnel.”

Need more detail? Barnett (September 8, 2020; Accessed September 9, 2020) gets to the heart of your new university policy. If your mission is to seed new knowledge to the world, your university can do this a lot better without exclusive patents.

Once the university’s culture has pivoted to open, technology transfer offices (downsized appropriately) could play a part in encouraging open and free licensing agreements that seed new knowledge out to the public. “Universities have, for a very long time, seen themselves primarily as dedicated to the advancement of knowledge and human welfare through basic research, reasoned inquiry, and education. The long-standing social traditions of science have always stood apart from market incentives and orientations. The problem is therefore one of reawakening slightly dormant cultural norms and understandings, rather than creating new ones in the teeth of long-standing contrary traditions” (Benkler 2006).

Research ideas are conversation starters. Openly discussed, they foster creative moments of serendipity. Patents are conversation enders. They lead to silence.

Life after patents

There are some universities, and hundreds of active academics — and associate vice chancellors and assistant deans — who have benefited financially (or defended their job salaries) from university-patent-driven technology-transfer practices enabled by these laws. There is an argument that universities need this new source of funding in the face of other budget cuts; that universities should realize immediate returns of the value of their research. However, such an argument already discounts other, and greater, value that open research might provide in the absence of patents. The larger corrective to current budget issues begins with a more complete understanding of the sum of the value of the public goods created by universities.

Newfield (2016) details the path to more fully optimize the value proposition for universities:

“We saw that the road to the public university’s decline was paved with a long, diffuse campaign against its status as a public good. The practical effects were disastrous. The demotion of public good status forced university managers to pare their institutions’ overall value to a narrow and fragile private fraction of the total (the wage premium over high school graduation). This paring undermined the university’s ability to deliver the indirect, nonmarket, and social benefits that make up the majority of its total value, and its ability to deliver the emerging private market good, which were creative capabilities, which paradoxically could not be supported by private good market calculations. The failure to make a strong case for both individual and mass creativity, which depended on rebuilt support for research as well as instruction, weakened the case for rebuilt public funding. Collateral damage includes weaker understanding of the public value of academic freedom for faculty, of due-process-based job security for all university employees, and of the need to convert student work time to study time. 

 The solution requires restoring the university’s public good status. A first step would be basic accounting reform that quantifies the value of indirect effects, nonmarket value, and social benefits with the same dutiful attentiveness that accounting applies to the private market benefit of higher salaries” (Newfield 2016).

Open science promotes multiple pathways for sharing new knowledge.

Open science looks to build on the longer-term historical/future mission of the academy as a wellspring for creative outcomes, both research and learning. All through this Handbook, you can find information and explanations on how open science cultural changes — many of which merely revitalize lost cultural norms — build innovation capacity and internal incentives that can drive science forward. Because of the compounding feature of open research collaborations, open science is likely to also improve the direct financial return on research within society, even though universities do not capture this return through patents.

Newfield (2016) proposes a multi-stage recovery from the neoliberal university. As open science works inside the academy to optimize the complete value of doing scholarship, this added value can become the subject of an active conversation with government funders and legislatures, who will be tasked to reinvest in the higher education endeavor as a public good with a solid value proposition.

Recovery from the private-goods-only evaluation of the university value proposition begins with a new appreciation of the larger creative capacity universities offer society. Open science harnesses this capacity, making it more available to public notice and, one hopes, support.

Your take on this

You can help your university become more open by working toward a post-patent culture. What is your sense, from your own experience working with and around university-held patents? How much of your time is spent dealing with demands for patentable technology transfer? Has the presence of existing patents caused you to shift your research topic? The future of patents is one more conversation within the cultural shift toward open science. Your ideas are valuable, perhaps these should be gifted to the academy (See: Idea Gardening).

“[M]any people, especially those in positions of influence, strive to ‘do things better,’ which in practice amounts to ‘do obsolete things better’”(Dindersmith 2018).

Is My Learned Society Obsolete?

This is just one question any open scientist hired on staff, or volunteering as a leader at a learned society needs to ask herself, and then others. In the last century and before that, learned societies flourished as homes for sub-disciplinary (and sub-sub-disciplinary) journals and annual conversations. They also provide a lobbying voice for their segment of the science endeavor, which itself may be obsolescent. They offer a place for new scholars to meet established scholars, and they recognize and reward exemplary work.

Michelle Brook <https://quantumplations.org/> did a count of learned societies in the UK, and found more than eight-hundred of these. Across the globe there are thousands. One can imagine that each one of these does some good within its purview. The other side of this good, however, is the opportunity cost of their work (members might get more value elsewhere), and their relative ability to contribute to and benefit from open science. Your learned society may not have changed much for decades. Remember telephone books (and “telephones”)? They were once essential. Now they are landfill. In the face of opportunities and change, all learned societies will need to find nimble footing going forward:

“[L]earned societies are part of the UK’s knowledge economy and they can expect to see the pace of change and external competition increasing, so having a forward-thinking, adaptable and change-welcoming culture is important to their future survival” (Gardner 2013).

If your learned society is, in part, or on the main, obsolete, then making these obsolete bits better is the wrong way to spend your energies. Instead, you need to start the process of replacing obsolete practices, behaviors, and attitudes. This will be hard, but there are good resources here in the Handbook to help you out. So, what does obsolescence look like in the academy?

Note: there are lots of other (organizational, attitudinal, etc.) ways that your society may be obsolete. Here we will just list some of the ones that impact its role in open science.

Seven obsolete features still found in learned societies:

1. Journals/monographs that are based on the production of a paper product, and distributed through a subscription model.

The form of a paper journal creates an arbitrary scarcity to the publication. It’s like you are publishing and privatizing the work of your members at the same time. University libraries are cajoled into subscriptions that must then be renewed for continuity (Also See: Learned Societies, Open Access, and Budgetary Cross-Subsidy <https://eve.gd/2019/09/17/learned-societies-open-access-and-budgetary-cross-subsidy/> Accessed September 17, 2019). Members are tasked to provide peer review. Most likely you already do a digital version, anyhow.

1. Step away from the cellulose and subscriptions and use the Internet. It’s been around for decades. Your scientist readers around the globe will approve. Do you pay for what you do (outside of publishing/privatizing the journal) with moneys from subscriptions? First, you’ve been charging too much. Second, if what you do (scholarships, prizes, lobbying) is valuable to your members then charge them a membership fee. There are new business plan ideas that can help wean your society from its subscription addiction. Check out Harvard’s Societies and Open Access Research (Accessed June 24, 2019). More than a thousand societies (Accessed August 2, 2019) already have switched to open access. Add your name to this list! Suggested citation here: Society Open Access Research (SOAR) Catalog. Suber, Peter; Sutton, Caroline; and Page, Amanda. January 2019. http://bit.ly/oaj-society. CC-BY-SA 4.0 International

2. Conferences with more than 300 participants.

This is not to say that you should not offer co-present events. If you are running a conference with a couple thousand members (or more, or many more), you are complexifying the central reason people have flown into your meeting: to find each other, and make new, or revive old, personal associations. Stepping into a big-city convention center with your colleagues may feel good, for about the first hour. After that it’s all random noise.

Your conference carbon footprint is inexcusable. You should respect and support members who pledge to not fly at all (<https://www.theguardian.com/science/2019/jun/29/no-flights-four-day-week-climate-scientists-home-save-planet> Accessed June 28, 2019)(See also: should climate scientists fly Accessed June 24, 2019). Find creative ways to split up large national/international meetings into a number of smaller meetings with better focus and a lot more interpersonal time.

3. Poster sessions with no digital archive.

Posters are a good way to show off work in progress, and an opportunity for small-group interactions. They do take a significant amount of time to produce, so they deserve a permanent home on the internet. If the way you do your poster session is obsolete, you can fix that. Find an open online platform for poster sharing and use it well. And use better poster design requirements (Accessed June 24, 2019; video here) to help start conversations.

4. Meetings dominated by plenary talks and sessions devoted to individuals presenting papers.

Apart from a few plenary talks curated to help the room consider new technologies and findings that stimulate conversations, most talks can be recorded and posted online before the meeting, and not take up the agenda. Instead, do workshops and panels that provoke discussions and learning moments. Use conversation models (such as the world cafe) to bring together early and late career professionals. Long breaks, good food, nearby coffee houses, and beer also help.

5. No support for member collectives/clubs.

A mailing list is not a “community.” You need to broadcast less and listen more. Your members need to find others working in very similar research arenas, and to have peer-to-peer online collaboration tools. Help them do this. Become research match-makers and coordination masters. That’s your new value proposition.

Are your association’s membership numbers a metric for you? Is bigger really better? Remember that the r/science sub-reddit <https://www.reddit.com/r/science/> has twenty million members. Theirs is bigger than yours. Go online and build active communication/collaboration resources for your members.

6. No support for cross-disciplinary assemblages.

Real innovation happens when people bridge between disciplinary/domain-specific silos. The least your society can do is open up its digital holdings to be indexed broadly by others. The clubs that your society supports need to find clubs in other societies to share their issues and problems. You can seek out venues and avenues for conversations (online or in person) with other societies. No society is alone in this.

7. Perhaps your whole learned society is obsolete. You may need to visit the “realm of chaos,” (See: The Work of Culture) perhaps at a board retreat somewhere, and rethink the entire purpose and vision for your society. If this society was created in a previous century to support a then-new sub-disciplinary journal, it may be a good time to pass this responsibility over to a pre-print repository and go home. Got some endowment money left over? Do a final meeting, make it free for graduate students and early-career folks, and challenge them to come up with the next best thing. Roll over the endowment to that. Got a big, fancy building for yourself? (Good for you!) Put it on the market, and use the funds to help all your employees find good jobs elsewhere.

“If you dislike change, you’re going to dislike irrelevance even more” (Eric Shinseki <wikiquotes> Accessed 12/28/19).

Don’t stay obsolete, be leaders for the future of your domain

Learned societies are well positioned to take leading roles in changing the behaviors of their members, though inclusive, member-led, reflexive culture change activities. They are often, however, keepers of a type of history that needs to be severed for them to move ahead. Their ceremonies and honors point to the past, to a graveyard of internally honored individuals (mostly white and male). They would do well to supplement their current honors with new ones that celebrate the future of their fields: early-career members, emerging sub-domains, collaboration successes, sharing and reuse.

Open Science Badges are Coming—and the New Nobel

“A ‘badge’ is a symbol or indicator of an accomplishment, skill, quality or interest. From the Boy and Girl Scouts, to PADI diving instruction, to the more recently popular geo-location game, Foursquare, badges have been successfully used to set goals, motivate behaviors, represent achievements and communicate success in many contexts. A “digital badge” is an online record of achievements, tracking the recipient’s communities of interaction that issued the badge and the work completed to get it. Digital badges can support connected learning environments by motivating learning and signaling achievement both within particular communities as well as across communities and institutions. This paper outlines and addresses a working set of definitions, ideas and guidelines around the use of digital badges within connected learning contexts” (Mozilla and Peer 2 Peer University 2012).

The notion of using open digital badges to acknowledge certain practices and learning achievements has been circulating in the open science endeavor for more than a decade. Over these years, this has become a perennial “near future” augmentation/implementation of how open science can recognize and reward practices and skills. Instead of using game-able metrics that rank individuals as though they were in a race, badges can promote active learning, current standards, professional development, and research quality assurance.

The transition from arbitrarily scarce reputation markers (impact metrics, prizes, awards) to universally available recognition markers also helps to level the ground on which careers can be built across the global republic of science. Every scientist who wants to take the time and effort to earn a badge for achieving some level of, say, research-data reusability, or graduate-student mentorship, can then show off this badge to the world. Every student/scientist who acquires a specific skill (R programming, software reusability, statistics, etc.) can add a new badge to their CV.

In education, micro certifications can augment diplomas and degrees by pointing to specific skills acquired during the course of study. A badge can signal the attainment of a prerequisite skill for taking an advanced course, say, or a capstone skill for outside employment. These badges can accumulate into suites of acknowledged skills that students can highlight for specific future occupations. Micro-level open badges can be assembled into practical certifications (Leaser 2016; Accessed August 14, 2020).

“Open Badges are a specific type of digital badge designed to promote learner-agency principles. Open Badges communicate skills and achievements by providing visual symbols of accomplishments embedded with veriTable data and evidence that can be shared across the web. Open Badges empower individuals to take their learning with them — wherever they go — building a rich picture of their lifelong learning and achievements journey. Thousands of organizations across the world already issue Open Badges, from non-profits to major employers and educational institutions at all levels” <https://www.imsglobal.org/digitalcredentials>; Accessed August 14, 2020.

Above: German carpenters carry a book for certifications of their work while they apprentice on the road for three years to become members of the guild.

Keeping current with the latest badge news is difficult. Several projects are moving ahead independently (sounds like open science in general). Start-up credentialing companies, spin-offs from the open-badge endeavor, are building online commercial services, some of them on a blockchain, for verifiable credentials. Their not-so-open badges help companies run internal educational services and streamline hiring for specific skills (See: https://info.badgr.com/).

You can check out open/digital badge resources on the web:

• Wikipedia: Digital Badges( <https://en.wikipedia.org/wiki/Digital_badge>; Accessed August 14, 2020),

• MIT initially created an open standard for blockchain-connected certifications (<https://www.blockcerts.org/>; Accessed August 14, 2020),

• Wikipedia: Mozilla Open Badges: (<https://en.wikipedia.org/wiki/Mozilla_Open_Badges>; Accessed August 14, 2020).

• The Mozilla effort was moved to IMS global where a standard for open badges is (currently) maintained (<https://www.imsglobal.org/activity/digital-badges>; Accessed August 14, 2020).

Of course, badges are not new. Philipp Schmidt (2017; Accessed August 17, 2020) points out a long, global history of verifiable certifications. Boy and girl scouts have used badges for a century or more. Academic diplomas are badges of learning, as are driver’s licenses (in theory).

Publishing with open badges for open practices

One place where badges might be implemented early is in open publishing, where publishers can add badges to their online descriptions of articles. These badges would serve to mark adherence to specific open practices. “Badges are an easy means of signaling and incentivizing desirable behaviors. Journals can offer badges acknowledging open practices to authors who are willing and able to meet criteria to earn the badge [(<https://osf.io/tvyxz/>; Accessed November 17, 2019)]. Badges acknowledging open practices signal that the journal values transparency, lets authors signal that they have met transparency standards for their research, and provides an immediate signal of accessible data, materials, or preregistration to readers. Badges allow adopting journals to take a low-risk policy change toward increased transparency. Compared, for example, to measures that require data deposition as a condition of publication, badge implementation is relatively resource-lite, badges are an incremental change in journal policy, and if badges are not valued by authors, they are ignored and business continues as usual” (Kidwell et al. 2016).

Learned Societies as badge engines

Each learned society could also host badges that members can earn by sharing their research or offering services to the membership. This is an easy way to displace current journal-based reputation markers, while acknowledging quality work, and boosting membership value. Societies can reward members whose work exemplifies those norms the society determines as core to their mission. Research that demonstrates team effort, active diversity, rigorous data collection, reusability — any practice that amplifies the value of the work for the society — might be connected to a badge. 

Unlike prizes, badges are open to all who meet the requirements; there are no losers here, except sloppy science. In the post-subscription business world, learned societies need to explore new value propositions. Badges are one way their communities can tap into their collective strengths to add real value to the lives of their members. At the same time, they recognize every member who qualifies, instead of awarding prizes to a selected few (See Also: Shaming the giant). How about this for a culture change practice: you acquire the right combination of badges and you automatically become a “fellow” of the society. You will have earned it; nobody needs to vote for you. 

If your learned society is not planning to offer badges, you might want to inquire about this. They are missing a golden opportunity. There should be a badge for that.

Badges for culture building

Badges are not easy to administer. Like all recognition schemes, they need to be well crafted and constantly tended to assure validation and verification. Badges focus attention on the practices and skills they announce. The governance of badge systems requires — as it also acquires — an active, reflexive cultural capacity to build trust and buy-in. One upside here, is that the work of supporting badges can also help an organization maintain its cultural norms over time. Badges help build communities. The conversations about badges can bring out the virtues and values of the group.

To change an organizational culture you first need to change the way things get done now. But how do you intercept current decision and work flows? How can you help the whole group unlearn toxic behaviors? Badges work to establish new paths for decisions and activities. They offer micro-rewards that nudge a community over to new practices. Badges can include learning requirements, exposing the whole community to relevant new information. Finally, earning a badge is a great occasion for a team mini-celebration. Even a skeptic with tenure can get a dose of good feelings when their team celebrates their recent achievement.

As badges celebrate cultural norms, they can help push toxic practices and other, external incentives into the margins. The badge system your academy organization creates can offer footholds up to the common goal of an open science destination; everybody can use the same badges to arrive at this shared future. There’s still a mountain to climb to get to “open”, only now there is plenty of room at the top, and a reliable path upward. Doing science right becomes easier when all the internal rewards are lined up. Buying into badges means buying out of current toxic conflicts-of-interest in the research flow (See: Building a gift economy: the dance of open science culture). It might be that open badges are the “killer app” for the future of open science!

The new Nobel: celebrating science events, their teams, and the history of discovery

“I won’t have anything to do with the Nobel Prize… it’s a pain in the… (LAUGHS). I don’t like honors. I appreciate it [my work] for the work that I did, and for people who appreciate it, and I know there’s a lot of physicists who use my work, I don’t need anything else, I don’t think there’s any sense to anything else. I don’t see that it makes any point that someone in the Swedish Academy decides that this work is noble enough to receive a prize — I’ve already got the prize….

 The prize is the pleasure of finding the thing out, the kick in the discovery, the observation that other people use it [my work] — those are the real things, the honors are unreal to me. I don’t believe in honors, it bothers me, honors bother, honors is epaulettes, honors is uniforms. My papa brought me up this way. I can’t stand it, it hurts me” (Feynman et al. 2005).

In their article, “Is the Nobel Prize Good for Science?,” Arturo Casedevall and Ferric Fang review the numerous controversies linked to Nobel Prize attribution. Their conclusions are here:

“In this regard, the Nobel Prize epitomizes the winner-takes-all economics of credit allocation and distorts the history of science by personalizing discoveries that are truly made by groups of individuals. The limitation of the prize to only 3 individuals at a time when most scientific discovery is the result of collaborative and cooperative research is arguably the major cause of Nobel Prize controversies . . . Changing the Nobel Prize to more fairly allocate credit would reduce the potential for controversy and directly benefit the scientific enterprise by promoting the cooperation and collaboration of scientists within a field to reduce the negative consequences of competition between individual scientists” (Casadevall and Fang 2013).

An uncommon commons populated by occasional giant ideas

As we explored above in The Work of Culture, in open science, scientists move regularly between the complex, emergent problematics of their object of study, the complicated process (in research and writing) required to extract knowledge from this, and the practices of open sharing. This means that the academy commons contains a whole lot of “uncommon” artifacts, pulled with great effort from the edge of knowing.

Scientists are also uncommon, made so by the demands of their profession. While their quotidian lifestyle is mainly long hours of very hard work, they have occasional days of unusual significance: the days when the months of research pay off with new knowledge. On these special days, all the work of their team and the entire history of their domain is rewarded with a new insight, pulled from indifferent data and mountains of observation. Scientists and their teams push back against the envelope of unknowns that surrounds our understanding of the universe until these unknowns surrender new understanding. In this way, scientists and their teams create the events (Badiou and Tarby 2013) that spark giant ideas.

The idea gets the prize

A giant idea, born from a moment of new knowing, perhaps in conversation, or in contemplation after conversation, or as suddenly emergent from the data, is the prize that science needs to celebrate; not the person who announces this, since the idea had been incubated by many within the larger commons. Celebrating the scientist here is like celebrating an obstetrician for having the baby, instead of for assisting in the delivery (“Great work, Doctor! Have you decided on a name for it yet?”). The baby, a giant new idea, birthed with some effort, might confirm and extend present knowledge with new information, or be the null result that corrects a widely held false scientific “fact,” or be an insight into a new theoretical space, hitherto unspoken.

Here, the collective “mother” could be the team, the room (See below), the adjacent now, a measure of luck, and the domain’s recent history. Yes, the scientist(s) here at the moment get to write up the news, but it’s really the idea, this new thing, that needs to be applauded. And it is also time to give mom her due regard when celebrating the child.

“Unmooring the prize from Alfred’s ‘the person’ bonds would happen if the physics prize were awarded to groups. This would reduce the pressure on scientists to stake their claims at the expense of others; it would offer a shortcut up the ladder of authority, a ladder some underrepresented, and thus less powerful, groups such as women and other minorities feel has already been pulled up out of reach” (Keating 2018).

Science wins the award

David Weinberger (2011) noted that “The smartest person in the room is the room itself.” All the conversations in this room reflect the genius of the room, not simply that of individual occupants. Open science rewards these giant ideas by sharing them instantly, globally, and with appreciation for their value and work it took to create the event that spawned them. Open science works to spread recognition across the science endeavor, being acutely aware of cumulative advantages for some and lifetimes of research done in obscurity for others. The latter deserve particular attention. Science at its best is not a personal heroic quest, but open, collaborative labor.

“The Nobel Prize fits with the narcissistic vision of science peopled by heroes, many of whom are very self-centred (but who of course can turn into nice and ethical people once they have succeeded). Science requires many different skills, and it is regrettable that recognition often goes to the storytellers or the dominant males of the community. By taking into account the tacit dimension, we could also better highlight the other key roles and skills — experimenter, tool constructor, organizer of databases — that hugely contribute to the progress of science” (Lemaitre 2015).

There are a lot of people pointing at several issues around the Nobel Prize and its method of selection; you can DuckDuckGo “Is the Nobel Prize obsolete” to get a list of articles with critiques and recommendations. The Handbook adds this topic here mainly to point out how science organizations can express their appreciation for great work by focusing on the science, not the scientists.

Devang Mehta puts it this way: “Here’s an even better idea: award the Nobel Prizes not to researchers but for discoveries. Imagine that today’s Nobel in physics was awarded for the discovery of gravitational waves, with no list of awardees, instead of awarding it to just three scientists out of hundreds. What of the prize money? Donate it to an international science fund to promote further research in each year’s prize-winning field of research. A science-oriented Nobel (rather than a scientist-oriented one) would both educate the public in the most important scientific developments and in turn stimulate new scientific progress by using the prize money to fund the next generation of researchers” (Mehta 2017; Accessed September 12, 2020).

The idea of giving out prizes is not itself obsolete; yet all award practices need to be refactored occasionally to capture the heart of the process of doing science, as this expands and changes in the coming decades. And, if it’s time to refactor the Nobel Prize, what does that suggest for the prizes your learned society hands out? Adding an ecosystem of badges (to show off skills and accomplishments) to the recognition landscape helps to replace prizes as a central feature of open science. Since prizes celebrate brilliant work, and as celebrations as a whole add positive affect to your culture, let the prizes continue. But give them some new thought. What is your idea for Nobel 2.0?