Bibliographic record
Abstract
While the spread of open access publishing for technical and scientific papers has improved some long-standing problems in the scientific publishing pipeline, it has worsened others. Meanwhile, peer review and the scientific publication system in general have come under increasingly intense criticism, provoking many reform ideas. Despite the strident language of reformists and the widespread opinion that the situation is worsening, reform ideas have generally received a lukewarm response among researchers. I argue that this complacency is a reaction to reforms that ignore the priorities of readers and how readers' needs have shaped the publishing world today. I outline a path for improving the peer-review system through the use of permanent review boards—to accommodate the needs of readers, reviewers, and authors alike—and show how to get from where we are now to where we should be.
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.
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 | 0.178 | 0.436 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.027 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.052 | 0.062 |
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".