Sustaining Scholarly Infrastructures through Collective Action: The Lessons that Olson can Teach us
Bibliographic record
Abstract
The infrastructures that underpin scholarship and research, including repositories, curation systems, aggregators, indexes and standards, are public goods. Finding sustainability models to support them is a challenge due to free-loading, where someone who does not contribute to the support of an infrastructure nonetheless gains the benefit of it. The work of Mancur Olson (1965) suggests that there are only three ways to address this for large groups: compelling all potential users, often through some form of taxation, to support the infrastructure; providing non-collective (club) goods to contributors that are created as a side-effect of providing the collective good; or implementing mechanisms that lower the effective number of participants in the negotiation (oligopoly).In this paper, I use Olson’s framework to analyse existing scholarly infrastructures and proposals for the sustainability of new infrastructures. This approach provides some important insights. First, it illustrates that the problems of sustainability are not merely ones of finance but of political economy, which means that focusing purely on financial sustainability in the absence of considering governance principles and community is the wrong approach. The second key insight this approach yields is that the size of the community supported by an infrastructure is a critical parameter. Sustainability models will need to change over the life cycle of an infrastructure with the growth (or decline) of the community. In both cases, identifying patterns for success and creating templates for governance and sustainability could be of significant value. Overall, this analysis demonstrates a need to consider how communities, platforms, and finances interact and suggests that a political economic analysis has real value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".