From amazing work to I beg to differ - analysis of bioRxiv preprints that received one public comment till September 2019
Bibliographic record
Abstract
Abstract While early commenting on studies is seen as one of the advantages of preprints, the nature of such comments, and the people who post them, have not been systematically explored. We analysed comments posted between 21 May 2015 and 9 September 2019 for 1,983 bioRxiv preprints that received only one comment. Sixty-nine percent of comments were posted by non-authors (n=1,366), and 31% by preprint authors (n=617). Twelve percent of non-author comments (n=168) were full review reports traditionally found during journal review, while the rest most commonly contained praises (n=577, 42%), suggestions (n=399, 29%), or criticisms (n=226, 17%). Authors’ comments most commonly contained publication status updates (n=354, 57%), additional study information (n=158, 26%), or solicited feedback for the preprints (n=65, 11%). Our study points to the value of preprint commenting, but further studies are needed to determine the role that comments play in shaping preprint versions and eventual journal publications.
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 | 0.054 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.024 |
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".