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Record W3094901197 · doi:10.1016/j.jaad.2020.10.027

Fairness and transparency in medical journals

2020· letter· en· W3094901197 on OpenAlexaboutno aff
Dirk M. Elston, Jane M. Grant‐Kels, Nikki Levin, Murad Alam, Emily Altman, Robert T. Brodell, Anthony P. Fernandez, M. Yadira Hurley, John C. Maize, Désirée Ratner, Julie V. Schaffer, Jonathan Kantor

Bibliographic record

VenueJournal of the American Academy of Dermatology · 2020
Typeletter
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsVettingMedicineTransparency (behavior)Focus (optics)Internet privacyFamily medicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Medical journals serve the public through vetting, dissemination, and archiving of scientific information. This letter will focus on some controversial trends, including prepublication, open reviews, and double-blind review.

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 imitation

Not 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.

metaresearch head score (Codex)0.293
metaresearch head score (Gemma)0.619
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.956
Threshold uncertainty score0.871

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2930.619
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0090.008
Science and technology studies0.0160.049
Scholarly communication0.0440.027
Open science0.0050.024
Research integrity0.0200.016
Insufficient payload (model declined to judge)0.0140.006

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.

Opus teacher head0.089
GPT teacher head0.425
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainEvaluation
GenreCommentary

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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