The Success of Open Access

Open Access is in my view a success story. It seems to be fashionable to flagellate ourselves about failures to reach 100% or bumps along the way, but over the course of the last 20 years we’ve gone from a state where most of the world’s literature was not accessible to one where most of it is.

When I started my research career you had to be on campus to read. At the beginning because everything was in print, and later because that was the only place with access. Also the only place with internet access but that changed relatively quickly. Today, while I technically can log into a university network I never do. The vast majority of academic literature I want to read is accessible, and the stuff I can’t? Well it doesn’t get read most of the time.

One of the problems with framing Open Access as a success story is that you have to acknowledge the positive aspects of publishing agreements in delivery high levels of access. The reality is that the countries that provide the highest levels of access to their research outputs are the same countries that have invested heavily in publishing agreements. This has made it easy for researchers and delivered on the policy goals, but it has come at a cost.

The Financial Risks for the Future

In recent work [1] Bianca Kramer and myself have looked at the financial aspects of publishing agreements and projected forwards what those costs might look like in various contexts. We posted a preprint on the methodology and have spoken about the modelling and its consequences based on publicly available data, with a focus on Sweden and Australia. We’ve also done work using non-public data in other settings and the patterns broadly hold.

The current trajectory of publishing costs are unaffordable.

There are a couple of ways forward out of this, but in the short to medium term it seems very unlikely that publishing agreements with their current scale and scope will survive. In Sweden, which we looked at most closely, the library consortium has already signalled a shift away from agreements supporting publishing in mixed open/closed journals (“hybrid”) towards only supporting publishing in purely open access journals (“gold”). Researchers could continue to publish in the more traditional hybrid journals, but to comply with Open Access policy they would need to find the resources to pay APCs. Swedish universities have some additional funding to support this but not enough to cover the projected costs. Grants can cover some but are under significant pressure as well.

In Australia, where we also have data on the specific outputs included within agreements, we see that agreements cover virtually all the hybrid outputs, but only 10% of those in purely open access journals. As you might expect researchers appear to choose to publish in venues where their costs are covered and no additional work is required. There are no immediate plans to radically change these agreements, but with costs projected to rise the money simply isn’t there to cover the projected increases long term.

The Researcher Burden and the Structural Risk

One of the core benefits of large-scale publishing agreements has been hiding all the administrative details from researchers. Provided the system works, we don’t need to worry because everything is taken care of for us. Open Access shifts from being an agenda we were supposed to be engaged with as researchers, supporting the  good of research and our communities, into background infrastructure, an invisible layer of support through which open access just happens.

What happens when that support goes away?

The most charitable projection is that researchers engage en masse and do the work, and, where necessary, provide any extra resource required, to achieve open access for their research outputs. This seems unlikely.

At the other extreme, the risk is that researchers simply continue to take the default. Large commercial publishers and the non-profit society publishers that choose to follow their lead, have consistently used default workflows to push researchers to the choices that suit the publishers’ interest. Here that will be to confront them with either paying for open access or agreeing to restrictions on repository deposit.

Dark patterns abound in journal submission workflows. Significant effort will be required to identify pathways to access and, at least based on history, the submission systems for many publishers won’t provide support.

As researchers we are both focused on the target (getting the output published) and lazy (seeking the quickest way through the submission system to achieve our goal). The likely outcome of a reduction in the scope and coverage of publisher agreements is therefore a significant, perhaps catastrophic reduction in Open Access levels.

The Repository Infrastructure and Staffing Gap

What about alternative pathways to access? In many settings rights retention and copyright exceptions are now in place, and that work continues with strong leadership from many libraries. This helps to provide the legal stability and clarity for repository deposit. In our work at COKI on citation diversity[2] we saw the suggestion that repository access is actually more strongly associated within increased citation counts and diversity. While citations are a very limited measure of use, this does suggest that utilizing repositories can provide a similar level of visibility, access and reuse as open access publishing.

But getting the appropriate version of an author’s accepted manuscript into a repository is also work, and frequently work that falls on the shoulders of one person within a university library. One consequence of the investment in publishing agreements appears to have been a lack of investment in both the technical infrastructure and staffing of repositories. There are specific local success stories and heroic efforts by many in the library system to protect, maintain and build capacity in these systems but, from the evidence we can assemble from the outside, that capacity is patchy and fragile across many countries that have invested heavily in publishing agreements. While publishing agreements have been associated with higher levels of open access publishing, the increase does not appear to flow through to overall levels of open access. Consistently we see a decrease in the proportion of outputs found only in repositories, leading overall to more modest gains in overall access levels and in some cases, no overall increase at all.

Looking more closely at repository provision we’ve also seen evidence of a lack of investment in local repository coverage at the university level. The data here is complex. The signals we see depend a lot on the technology systems for repositories and the specifics of which sites are being harvested by OpenAlex, and the extent to which this can be linked to affiliations though PIDs or URL patterns. But overall the picture is grim, with many records in repositories appearing to be metadata only and coverage of the institution’s own outputs in full text limited, even for those published Open Access under a CC BY license.

The risk

The structural risk here is that a combination of under investment in infrastructure and a focus on delivering access without requiring researcher engagement leads to a substantial backwards slide. We can make some estimates of the amount of access delivered via publishing agreements by extrapolating from the Australian and Swedish cases. A worst case scenario is agreement cancellation and no take up of the slack by researchers (and institutions) utilising and backing the capacity that is there in the library systems. The best case scenario is a mass shift by researchers to self-archiving or adoption of non-APC open access publishing venues. Neither of the extremes is likely but drops at national level in open access proportion of 10-30% are plausible. 

Sweden could drop back from ~90% to 60 or 70%. Australia from around 70% back to below 50%. Extrapolating trends to other countries,  the UK could drop from around 80% to 60% and the Netherlands from 90% to 70%. For the latter two interesting questions arise whether the efforts in rights retention and secondary publishing rights (led often from libraries again) and associated work will help to reduce or displace any such drop. 

These are catastrophic falls. From it being a reasonable approximation that “most new scholarly content is available” we could rapidly reach a tipping point where that is no longer a viable position. In combination with the potential for financial retrenchments at large publishers as their parent companies shift away from content to whatever shiny technology they are chasing this week, or when the AI bubble bursts, the entire system is not stable. 

The academic community is not well placed to weather the potential storms in front of us.

The mitigations

I would have liked to have a subject heading of “solutions” but it is perhaps better to start with mitigations of the worst consequences, with the goal that if they are well implemented they can turn into solutions over time. I also want to focus on things within the control of the academic community and research institutions today. 

The key action is a re-engagement of researchers with the principle and practice of Open Access. There are real opportunities here. The AI hype has created some negative perceptions of re-use but also focused researcher attention on what we are allowed to do. Digital and online teaching has forced researchers to think about the licensing of figures we use in our lectures. The crushing weight of over-work means that the ease of access to scholarly content when at home and travelling is experienced as a massive reduction of friction.

Of course, it is easy to forget this as the grind increases and the overall experience is of a “less worse” situation. Positioning the library and information services as creating space, and if necessary in alignment with researchers over leadership is one approach that could work. 

Creating that space includes making repository deposit as painless as possible. The lack of investment in repositories (and arguably the over-investment in corporate controlled, vertically integrated CRIS systems) has left deposit, and particularly mediated deposit as a painful process in most settings. This is not merely a technical investment but also crucially a staff investment. In many settings repository management has dropped in resourcing to a few FTE if not a single person. The expertise and experience of that remaining person will be critical to retraining staff, engaging researchers, and identifying improvements.

At the core this is about being serious about protecting and reinvesting the marginal savings that occur when agreements and subscriptions are cancelled, and not losing those to debt servicing and consolidated revenue. In today’s world that will be a battle, but protecting the existing budget is a starting point. Examples of reinvestment from France (at least as it was) via the Sorbonne to Tromsø offer some direction here.

Engagement programs and repository support both require resources. The challenge will be to capture resources released from agreements and ensure their reinvestment. The actual amounts needed are modest in comparison to current spend, but then they always are. Coordination across institutions also requires investment and is woefully under resourced. What coordination there is generally depends on informal networks heroically trying to hold things in alignment (you know who you are).

References

1. C. Neylon, and B. Kramer, "Trajectory modelling of publishing costs using Open Research Information - An open source analytics pipeline", 2026. https://doi.org/10.5281/zenodo.20957444
2. C. Huang, C. Neylon, L. Montgomery, R. Hosking, J.P. Diprose, R.N. Handcock, and K. Wilson, "Open access research outputs receive more diverse citations", Scientometrics, vol. 129, pp. 825-845, 2024. https://doi.org/10.1007/s11192-023-04894-0

This was originally a script for a video I recorded for a meeting of the Australian Deans and Directors of Creative Arts (DDCA) group. It is addressed to those who are in institutional positions who need to make judgements about the qualities of research outputs in Humanities and Social Sciences with a particular focus on outputs that are “non-traditional” (actually often very “traditional” in their field!) or otherwise “not easily measurable by a STEM audience”.

In my view there are two key roles for those specifically engaged in supporting those doing HSS research. The first is to advocate for financial systems that can support small and numerous research projects and the funders who want to support them. The second key role is to defend the diversity of research practices, outputs and impacts in HSS and to create a meaningful narrative around their qualities for institutional audiences.

I’m going to focus on the second here, but I wanted to link both to underline the point that what is of value in HSS research is precisely its diversity, and its difference from the homogeneity and scale of conventional STEM research and its outputs. There is not infrequently a greater diversity of research outputs from single HSS groups, centres and departments than there is from entire faculties of science, engineering and medicine.

That diversity is at the core of the value that HSS research and scholarship brings to the table. 

Every social epistemology of knowledge from Latour to Longino via Merton, Ravetz, Kuhn and Fleck have at their heart the idea that the core is the negotiation of knowledge across boundaries, or between groups. This places diversity, of all kinds, at the heart of this process, and linked to it the institutional systems that keep it alive. Arguably it is the purpose of a university to support and bring into contact diverse approaches, experiences and ways of knowing.

The issue of course is that managing that diversity is a challenge. At the scale of a university, systems are a necessity. Consistency is needed and economies of scale are real. Particularly in HSS but across other disciplines as well, it is impossible for any leader to be across the detail of how good every specific body of work is. If you believe in the argument for diversity then this as it should be – anything else would be a sign of insufficient diversity. That doesn’t mean that leaders can’t be effective at telling the stories about why diverse research matters. Nor does it mean that they can’t make judgement calls on the distribution of resources. But it means those decisions are subjective and contextual. And that the evidence that supports them will be specific to each case.

But that doesn’t solve the problem either. So what are the pragmatic and principled approaches available to addressing this complexity? In my view the key lies in separating out the challenge into two parts, one relatively straightforward and immediately useful, the other more complex but I would argue ultimately tractable.

An important, but oddly overlooked distinction in research evaluation and particularly technology discussions is the distinction between tracking outputs and evaluating them. There is often a tendency to lump these together and to imagine for example, that if scholarly books were better tracked then that would necessarily mean there would be improved citation data for them. The homogeneity of STEM research outputs means that issues with data coverage and completeness are often conflated to the question “is it indexed” using the presence of a specific journal in a specific database both as a reliable marker of quality itself but also of confidence in the quality and relevance of generally accepted performance data.

So, although this is an oversimplification, in STEM the tracking of outputs is standardised and homogenous. You look it up in an index like Web of Science.

In HSS, and particularly for creative works, the situation is far more complex. Institutions generally have very poor data on the scope and volume of creative outputs, or other forms of (so-called) “non-traditional” outputs. Work done by Niamh Quigley, within the COKI team (Quigley, 2022) showed generally used recording systems (systems like Symplectic, Pure and the like) tended to be poor at capturing aspects of research outputs important to creative researchers. Further, there was a tendency for the information that was manually inputted by researchers (that they care enough to type in!) to be stripped away as it made its way through institutional systems to the data warehouses used for high level analysis.

From a systems perspective what is needed is systems that make it easy for creative practice researchers to register outputs, to flexibly record the evidence that they consider important about the qualities of these works, and to also make it easy for institutions in turn to gather that information up. Recent technical developments provide improved paths to making this happen.

The Open Research Contributor and ID (ORCID) system provides a mechanism for researchers to authoritatively identify themselves, and to record outputs they have contributed to. In 2025 ORCID have increased the number of work types (Petro, 2025) to include a range relevant in humanities (including images, video, designs, sound and other creative works) as well as improving the mapping of some other relevant work types including reports. A researcher can choose what works they make visible, and thereby designate as “research”. This is an important distinction for some creative workers. Institutions can then harvest those outputs without requiring further work from researchers. 

ORCID doesn’t solve all the problems though. Badly implemented as an institutional system it is yet another place creative researchers have to battle through manually adding their work while colleagues in STEM subjects have it all done for them by journal publishers. Critical to making this work is a commitment from you and your teams that ORCID be the only place researchers have to make sure their work is available and pulling that information from ORCID is an institutional responsibility.

But that still leaves the manual input issue. There isn’t – currently – a complete solution for that (although one – or several – could be built) but there is at least a solution that offers some additional value for creative researchers. Zenodo is a large scale repository, managed by CERN (yes the particle accelerator in Switzerland CERN) that offers a place for anyone to deposit research outputs. It provides a safe, publicly accessible platform from which researchers can showcase their work. It even allows for dark deposit for those creative researchers who need to restrict access to the core of their work for artistic, ethical or commercial reasons.

Most importantly Zenodo provides DataCite DOIs and this means that – while you do have to manually put information into Zenodo – that information can automatically flow through to ORCID. One place to input the information and then it can flow to the other places it needs to be.

This flow of information should be the case for any repository that integrates with DataCite (or Crossref) to provide DOIs. Zenodo is large scale and flexible, but there can be an argument for specialist repositories. I am generally sceptical of the value of creating new local repositories, whether at institutions or nationally, but the key questions to ask of any repository are “does it integrate with DOI providers” and “does it provide the recording functionality creative research practitioners need”? That flexibility is key and Zenodo has some strengths here that can also help us to address, if not yet fully solve, the second problem. 

Creative practice researchers know the reasons that their work is important, and can help provide evidence of that. But that evidence is highly diverse. 

I know the pressures that institutional leaders face to articulate a comparative quantitative value of research outputs to the rest of the university. And I would argue that we nonetheless need to collectively hold the line that this makes no sense. If you value a diversity of research and researchers, you can’t protect that diversity by homogenising the way that value is measured. 

But that doesn’t mean we can’t articulate value. It doesn’t mean we can’t compare how disparate research projects and outputs achieve specific goals. It just means, in the best tradition of HSS scholarship, that the evidence that we use is contextual. That it is diverse.

And here is how repositories like Zenodo can help. Where they support the creation of packages of work, researchers can bundle up the work itself with evidence of its value and impact. This will start unstructured, but over time, and in collaboration with research communities it will be possible to develop some standards. For specific cases. Where appropriate. This might mean giving some background on the gallery that invited an exhibition, the footfall or an audience survey. It might be details of the film festival that selected a documentary for screening, or audience numbers when it was syndicated or broadcast. It might be structured in a particular way to make it easier to process (or even generate). But the key is to build on systems that always allow for the flexibility of a narrative statement that allows for the flexibility that research statements already provide. 

This workflow places researchers in control, gives them flexibility they need to provide access and to evidence the value of their work. It provides a single point of entry from which communities, institutions, and others can draw in the data to help track those outputs and collate the evidence of their value. It is not controlled by corporate interests but by academic communities. It is not subject to the funding whims of one national government but an international set of consortia.

Perhaps best of all, this is an area where the humanities and social sciences can lead. It solves a problem by putting researchers in control while supporting institutional needs. It helps and simplifies monitoring while offering a path to gathering more sophisticated evidence to tell better stories. These are not problems that are restricted to HSS, but you could argue that much of STEM evaluation has lost its way, focusing on numbers instead of substance, journal lists instead of journal content, impact factors instead of actual impact. This recontextualisation, and re-situation is where humanities and social sciences scholars, and leadership can show a way to better forms of evaluation.

With the world in its current state, there is a wide interest in both how we preserve what is good in research systems and make necessary improvements. Ensuring sustainability and quality of support in the organisations and communities that make up the research landscape is crucial. But there are also deep problems in our communities and organisations, including structural issues that lead to poor practice and fraud, deep and ongoing inequities and exclusions, and failures in the mentoring and support systems that are needed by the next generations of researchers.

It was against this background that we started a workstream to look at indicators of research culture and environment within the RoRI AGORRA project, culminating in a new Working Paper being published today. AGORRA (standing for A Global Observatory of Responsible Research Assessment) is a project focused on understanding and cataloguing national evaluation systems in different parts of the world. By working closely with the funder representatives on the AGORRA team, we were able to ground our research in a diverse set of national contexts, which shaped the ultimate focus of the paper.

At the beginning I was keen to ensure we try to take a wide perspective on what was meant by “research culture” and “research environment”. One that was inclusive of the differing contexts represented in AGORRA and RoRI more generally. These terms were being used in the UK as part of the REF (which has included aspects of “research environment” previously) with a pilot on “People, Culture and Environment” running in parallel with our workstream. I was also keen to get a perspective on where (and whether) the terms were being used in other settings.

Culture and environment are used to focus on different issues in different contexts

What was clear was that — within contexts of research evaluation or monitoring — the terms “research culture” and “research environment” were used to focus attention on different (but often overlapping) issues in different contexts. The demographic diversity of research staff was a common, but not universal, issue. In some contexts, research practice and integrity were the most significant issues linked with “culture”, but in others these were one of many. “Environment” could refer to the qualities of career structures, the availability of financial or digital resources, or the safety of buildings, alongside the presence of policy instruments. 

We don’t always mean the same things by “environment” and “culture”. They are used in different ways in different contexts and often without a clear definition. Environment tends to refer to structures and organisations, and culture to communities and people, but there were variations. This makes sense; one person’s culture (say that of a senior researcher in a specific organisation) might be experienced by another as part of the environment in which they have little say (e.g. a junior researcher, or a community stakeholder like an interested patient).

What we want to preserve and what we want to change are often in common but not universal

There were many “areas of concern” that were common across contexts, but none were a universal priority. Research practice, demographic diversity and inclusion, resourcing, career structures and opportunities, researcher mobility, wider engagement and open communication were all areas that appeared in multiple contexts. 

When it comes to examining these there are many existing indicators that might be applicable in a monitoring or evaluation context for specific areas of concern. Most of these come from two places. The first is existing reporting requirements for organisations, either imposed by government, or as part of programs such as ATHENA Swan focusing on gender diversity and inclusion. The second source is the monitoring of research outputs. Both sources provide a foundation for developing improved indicators.The concentration of these existing and potential indicators in two areas, and the fact that they are relevant to specific areas, leads to a question: is there a way to put indicators in context and define gaps? We need a framework that will help us relate them to one another. For example, there are many existing indicators which might be useful for examining organisations, but very few of these could currently be applied to communities. The data simply isn’t reported that way.

Ostrom’s IAD framework as a tool to understand relationships and gaps

This aspect of communities and organisations plus the presence of many output-based indicators lead us to Ostrom’s Institutional Analysis and Development framework (IAD). IAD is used to understand how characteristics of a setting (made up of organisations, communities and the formal and informal rules they use) come together in specific interactions (called an “action arena”) to generate outcomes which  through a process of feedback change the organisations, communities and rules that make up the setting.

The IAD framework reminds us that what matters here is how the feedback from monitoring and evaluation processes is received and leads to change, or leads to stabilisation. While we have many indicators of specific areas of concern, we have few if any that help us to understand whether organisations and communities are ready to process feedback productively. Do they have the capacity to engage with it, and if so, make good use of it?

The project both defined many indicators that might be useful in specific contexts but also, as a result, that no single indicator (or even a “basket” of them) can capture the complexity of research cultures and environments. Whether the policy goal is change or stabilisation, the IAD framework shows we need to consider how any indicators we do implement are received, and be ready to adapt, change or abandon them if they lead to adverse responses.

Tap…tap…is this thing still on…..SKKRRREEEEEEEEEEEEEE

Consider this yet another commitment to trying to write here a little more regularly. Lots of thinking has been going on but not much writing! At least not writing as I’m going…

Three things colliding over the past few weeks have led me to want to try and get some ideas down. The first was a conversation – one of a set really – with Titus Brown and Dan Katz relating to the application of political economy and collective action theory to communities building research.

The second was the posting of video of a talk I gave a month or so back to our research centre, the Centre for Culture and Technology at Curtin University. In this, I tried to work through the full story of some recent thinking for the first time. I’m trying to articulate what has evolved in my thinking from a view driven by network theory that open is good, more open is better, to an understanding of where and how things have gone wrong.

The final piece was the publication of a new paper (preprint version is also available, actually its what I read so that’s what I’ll link to) by Sabina Leonelli which tackles the value of reproducibility as a concept head on. Leonelli’s work has been a big influence on my developing thinking. Two pieces are particularly relevant. The idea that data is not a thing, but is a category defined by use. That is, we can’t say whether a particular object is data, only that something is data when it is used in particular ways. This is true of many things in research, the objectivist in us wants the qualities of objects like data, methods, and knowledge itself to be eternal platonic concepts inherent in the objects themselves. In practice however these qualities only reveal themselves in certain activities.

These activities depend in turn on ‘repertoires’ another concept I’ve taken from Leonelli’s work, this time from a paper published with Rachel Ankeny. The concept here is that research communities are defined by practices and that these practices are not just learned, but displayed and internalised, in a similar manner to a musician learning repertoire as a set of hurdles to be jumped in their development. I first read this paper at the same time as I was reading Lave and Wenger’s Situated Learning and the parallel between craft learning (see also Ravetz), identity making (see also Hartley and Potts) and community had a significant resonance for me.

In the new piece Leonelli tackles the range of different, and largely incommensurate meanings that are ascribed to the concept of ‘reproducibility’. The paper tackles a range of disciplinarily bound practices ranging from computational sciences, where there is ‘total control’ (yes I could quibble about machine states and compilers but this is philosophy, let’s accept the abstraction) through to ethnography, where there is the expectation that the life experience of the researcher affects the outcome, and that a different researcher would reach different conclusions.

The article focuses on how the necessity for documentation changes as different aspects of accountability rather than reproducibility are taken into account. These concepts of responsibility and accountability for sources of variability that have implications for the claims being made seems more productive and more readily contextualised and generalised. The article finishes with a call to value ‘epistemic diversity’. That is, it is through differing kinds of cross referencing, with different sources of variability, accountability, and reliability that we can most usefully build knowledge.

All of this resonates for me because I’ve been working towards a similar end from a different place. My concern has been thinking about how to operationalise social models of knowledge production. There are a broad class of constructivist models of knowledge where validation occurs when groups come together and contest claims. I have a paper currently in review that casts this as ‘productive conflict’.

In such models the greater the diversity of groups in contact the more ‘general’ the knowledge that is created. However the more diverse those groups are, the less likely it is that such contact will be productive. Indeed it can be harmful – and in most cases involving disadvantaged groups (which is practically everyone in comparison to a professional, permanently employed, western academic) it is at the very least a significant burden and frequently significantly damaging.

In the talk I’m trying to work through the question of how such contacts can be made safe, while equally privileging the value of closing ranks, returning to community to regain strength and re-affirm identity. If diversity is a first order principle in the utilitarian goal of making knowledge, what are the principles of care that allow us to protect the communities that contribute that diversity?

Part of the argument I make is that we need to return to the thinking about network structures. Five years ago I was making the case that ‘bigger is better’ underpinned by an assumption that even if benefits flowed to those who were already well connected the new opportunities of scale would still spread benefits more widely. That seems less and less to be the case today, and the key problems appear to be ones where the benefits of locality are reduced or broken, boundaries are too readily traversed, and groups come into contact in non-productive conflict too frequently.

In the talk I discuss the need to privilege locality. This is the opposite of what most of our systems do today, both in the wider world (‘you need to go viral!’) and in academia (‘international rankings! international journals!’). Many social models draw simple lines with groups inside or outside. Even with a mental view of groups within groups and overlapping groups this tends to miss the messiness that results from non-homogenous interactions across group boundaries. Network structural analysis is harder to do and harder to visualise but it seems to me crucial to get a view of what the optimal dynamics are.

One way to frame the question of network and community dynamics is economically. That is, to ask how the differing groups and communities in contact are sustained and succeed or fail. This in turn is a question of exchange (not necessarily purely financial exchange, but there are limitations to this framing!). Models of club economics and collective action from political economy are useful here and this leads us back to the first conversation I mentioned above.

Titus Brown has been working through a series of posts, relating to the NIH Data Commons project looking at the goods produced in open source projects through the lens of commons and clubs goods. Dan Katz was raising good questions about issues of time and scale that are not well captured by the classical analysis of goods.

I have quibbles with Brown’s analyses and often with others who use this kind of goods-class analysis. However I only just realised that my issue was similar to Leonelli’s point about data. The question of what is ‘the good’ in any particular case is relational, not an absolute. Our discussion of these models tends to turn on an implicit assumption that something (prestige, code, knowledge, expert attention) is the specific good in play, something that in turn surfaces a common assumption in most classical economics that it is appropriate to assume some kind of ‘numeraire good’ that allows exchange of all the others (this is what money is for, and if there’s anything wrong with economic analysis, its money..).

This doesn’t leave me with any firm conclusions beyond some possible routes to analysis. It seems likely that simple analysis based on static descriptions of community and the environment aren’t going to give us answers. We need to understand dynamics of groups opening up and enclosing over time, what sustains them, and what they might find valuable in exchange. We need agent based modelling of the network structures these occur over, and ways of translating from local structure in networks to more ‘bright-lined’ social models of groups. We need to move beyond the simple ‘economic mathematics’ that is often developed from the the club economics of Buchannan and Olson and to develop much more sophisticated and flexible models that allow the nature of exchange and goods to change when viewed from different perspectives.

All in all it emphasises the conclusion I’d reached, that diversity is a first order principle. All forms of diversity and the more diverse the better, provided the coordination between groups is principled. There’s a lot to unpack in that word ‘principled’ but I feel like its the link to the ethical dimension I’ve been missing. I need to dig deeper into notions of ‘care’ (Moore, Priego) and ‘flourishing’ (Holbrook) to tease this out.