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Record W3017519019 · doi:10.1126/science.abc1731

Against pandemic research exceptionalism

2020· article· en· W3017519019 on OpenAlexaff
Alex John London, Jonathan Kimmelman

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMcGill UniversityEthica (Canada)
Fundersnot available
KeywordsExcuseExceptionalismPandemicPolitical scienceCoronavirus disease 2019 (COVID-19)Law and economicsLawEconomicsMedicinePolitics

Abstract

fetched live from OpenAlex

Crises are no excuse for lowering scientific standards

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)0.077
metaresearch head score (Gemma)0.186
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.923
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.186
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0100.042
Scholarly communication0.0170.021
Open science0.0040.019
Research integrity0.0350.059
Insufficient payload (model declined to judge)0.0190.005

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 head0.694
GPT teacher head0.602
Teacher spread0.092 · 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 designTheoretical or conceptual
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

Citations387
Published2020
Admission routes1
Has abstractyes

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