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Record W4287923646 · doi:10.20529/ijme.2022.052

Whistleblowing without names is hearsay

2022· letter· en· W4287923646 on OpenAlexaff
David Healy

Bibliographic record

VenueIndian Journal of Medical Ethics · 2022
Typeletter
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHearsaySAFERAnonymityComputer securityInternet privacyComputer scienceTerm (time)Risk analysis (engineering)Law and economicsBusinessLawPolitical scienceSociology

Abstract

fetched live from OpenAlex

Peter Gøtzsche's proposal to provide anonymity to certain people reporting bad practices within the pharmaceutical industry, regulatory apparatus or health systems is superficially appealing but likely to generate more problems in the longer run than it might solve in the short term. We need to analyse what features of our systems generate problems and correct those, rather than rely on insiders to report on the resulting problems, as all these reports do is offer a false sense of security that things are safer now that one problem has been identified.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.102
metaresearch head score (Gemma)0.271
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1020.271
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0160.397
Insufficient payload (model declined to judge)0.0800.001

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 head0.147
GPT teacher head0.522
Teacher spread0.375 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
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

Citations4
Published2022
Admission routes1
Has abstractyes

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