Pubcreds: Fixing the Peer Review Process by “Privatizing” the Reviewer Commons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 it