Sustaining Scholarly Infrastructures through Collective Action: The Lessons that Olson can Teach us
Bibliographic record
Abstract
Abstract Infrastructures for data, such as repositories, curation systems, aggregators, indexes and standards are public goods. This means that finding sustainable economic models to support them is a challenge. This is due to free-loading, where someone who does not contribute to the support of the infrastructure nonetheless gains the benefit of it. The work of Mancur Olson (1974) suggests there are only three ways to address this for large groups: compulsion (often as some form of taxation) to support the infrastructure; the provision of non-collective (club) goods limited to those who contribute as a side-effect of providing the collective good; or mechanisms that lower the effective number of participants in the negotiation (oligopoly). In this paper I use Olson’s framework to analyze existing scholarly infrastructures and proposals for the sustainability of new infrastructures. I argue that the focus on sustainability models prior to seeking a set of agreed governance principles is the wrong approach. Rather we need to understand how to navigate from club-like to public-like goods. We need to define the communities that contribute and identify club-like benefits for those contributors. We need interoperable principles of governance and resourcing to provide public-like goods and we should draw on the political economics of taxation to develop this.
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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".