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Record W4213413894 · doi:10.1126/science.abn0675

Streamlining ethics review for international health research

2022· article· en· W4213413894 on OpenAlexaff
Mark A. Rothstein, Ma’n H. Zawati, Adrian Thorogood, Michael J. S. Beauvais, Yann Joly, Kyle B. Brothers, Michael Lang, Pamela Andanda, Calvin Wai-Loon Ho, Rosario Isasi, Jane Kaye, Won Bok Lee, Obiajulu Nnamuchi, Andrea Saltzman, Bartha Maria Knoppers

Bibliographic record

VenueScience · 2022
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsEngineering ethicsResearch ethicsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Single-site review means protection and efficiency.

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)0.641
metaresearch head score (Gemma)0.735
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.359
Threshold uncertainty score0.442

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6410.735
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0060.005
Science and technology studies0.0070.027
Scholarly communication0.0260.030
Open science0.0070.025
Research integrity0.0180.029
Insufficient payload (model declined to judge)0.0100.006

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 head0.965
GPT teacher head0.820
Teacher spread0.145 · 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
DomainMethods
GenreEmpirical

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

Citations9
Published2022
Admission routes1
Has abstractyes

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