Bibliographic record
Abstract
MODERATOR: Heather R Staines Independent Consultant Trumbull, Connecticut SPEAKERS: Hannah Drury Product Manager eLife/Sciety Peterborough, United Kingdom Samantha Hindle Content Manager bioRxiv and medRxiv Cofounder PREreview Stefano M Bertozzi Dean Emeritus and Professor of Health Policy and Management UC Berkeley School of Public Health Gunther Eysenbach CEO and Executive Editor JMIR Publications Toronto, Ontario, Canada REPORTER: Tony Alves Hopedale, Massachusetts The session “Overlay Journals, Overlay Reviews: Has Their Time Finally Come?” was held virtually on May 4, 2021. Moderated by Heather Staines, Senior Consultant at Delta Think, the session featured presentations by Stefano M Bertozzi, Dean Emeritus and Professor of Public Health Policy and Management at UC Berkeley; Gunther Eysenbach, CEO and Executive Editor, JMIR Publications; Samantha Hindle, Content Manager of bioRxiv and medRxiv and Co-founder of PREreview; and Hannah Drury, Product Manager of Sciety at eLife. COVID-19 has accelerated the use of preprints, and researchers and media are increasingly turning to preprint servers to get an early glimpse at new studies. Preprint servers have come under increased scrutiny, and many have risen to the challenge by implementing various forms of peer review. Another interesting and related phenomenon is the increase in “overlay journals,” which use “overlay reviews” to help validate the science in preprints, thus increasing trust and transparency in preprints. If you are unfamiliar with the concept of overlay journals, they are a type of online, open access compilation of preprints, public domain publications, and already-published open access articles. Sometimes the compilations are thematic, addressing specific topics, and […]
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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 | 0.072 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.145 | 0.089 |
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".