Bibliographic record
Abstract
Scholarly peer review is crucial to science: it not only determines what is published where, but also, indirectly, who is hired, funded and promoted. Yet, virtually every academic has peer review horror stories. Empirical evidence suggests that "peer review is prejudiced, capricious, inefficient, ineffective, and generally unscientific" [1]. An experiment at a major machine learning conference found that peer review was unreliable highlighted that the outcome of peer review can be very noisy [2, 3]. In May 2019, ACM SIGSOFT launched an initiative to improve the quality of research papers and peer reviews at software engineering venues. It has two main components: empirical standards and recommendations for improving review processes.
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 | 0.122 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.032 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.052 |
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".