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Record W3149236319 · doi:10.1159/000514537

Barriers to Diversity and Academic Promotion in Dermatology: Recommendations Moving Forward

2021· article· en· W3149236319 on OpenAlexaff
Justin D. Lu, Irina Sverdlichenko, Javed Siddiqi, Faisal Khosa

Bibliographic record

VenueDermatology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsVancouver General HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsDiversity (politics)Promotion (chess)DermatologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Barriers to gender

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)0.071
metaresearch head score (Gemma)0.184
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.929
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.184
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0050.006
Science and technology studies0.0140.011
Scholarly communication0.0280.027
Open science0.0120.025
Research integrity0.0410.049
Insufficient payload (model declined to judge)0.0360.004

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 head0.043
GPT teacher head0.323
Teacher spread0.280 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
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

Citations18
Published2021
Admission routes1
Has abstractno

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