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Record W1530178108 · doi:10.1186/s12940-015-0035-6

Scientific journals and conflict of interest disclosure: what progress has been made?

2015· article· en· W1530178108 on OpenAlexaff
Kathleen Ruff

Bibliographic record

VenueEnvironmental Health · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsCanadian Nurses Association
Fundersnot available
KeywordsConflict of interestPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

The article addresses the failure of the scientific community to create an effective mechanism to protect the integrity of the scientific literature from improper influence by vested interests. The seriousness of this threat is increasingly recognized. Scientists willing to distort scientific research to serve vested interests receive millions of dollars for their services. Organizations such as the International Committee of Medical Journal Editors, the World Association of Medical Editors and the Committee on Publication Ethics (COPE) have launched initiatives to establish international standards for Conflict of Interest (COI) disclosure. COPE requires its 7,000 member journals to comply with its Code of Conduct for Journal Editors. While these initiatives are encouraging, they are internal educational endeavours only. Five examples are given showing failure of COPE member journals to comply with COPE's Code of Conduct. While COPE offers a complaint process, it involves only discussion and voluntary compliance. COPE neither polices nor enforces its Code. Instead of the current feeble, un-resourced process, which delivers neither transparency nor accountability, the article proposes the creation of a mechanism that will employ specific, effective measures to address contraventions of COI disclosure requirements.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.738
GPT teacher head0.569
Teacher spread0.169 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations56
Published2015
Admission routes1
Has abstractyes

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