Pubcreds: Fixing the Peer Review Process by “Privatizing” the Reviewer Commons
Bibliographic record
Abstract
Abstract The peer review system is breaking down and will soon be in crisis: increasing numbers of submitted manuscripts mean that demand for reviews is outstripping supply. This is a classic “tragedy of the commons,” in which individuals have every incentive to exploit the “reviewer commons” by submitting manuscripts, but little or no incentive to contribute reviews. The result is a system increasingly dominated by “cheats” (individuals who submit papers without doing proportionate reviewing), with increasingly random and potentially biased results as more and more manuscripts are rejected without external review. Because this is a classic tragedy of the commons, we propose a classic solution: privatizing the commons. Specifically, we propose that instead of being free to exploit the reviewer commons at will, authors should have to “pay” for their submissions using a novel “currency” called PubCreds, earned by performing reviews. We discuss how this simple, powerful idea could be implemented in practice, and describe its advantages over previously proposed solutions. While our proposal may seem radical, doing nothing will lead to a system in which external review becomes a thing of the past, decision‐making by journals is correspondingly stochastic, and the most selfish among us are the most rewarded.
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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 | 0.055 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".