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Record W2941337565 · doi:10.1007/s11673-019-09908-2

Editors Should Declare Conflicts of Interest

2019· article· en· W2941337565 on OpenAlexfundno aff
Jaime A. Teixeira da Silva, Judit Dobránszki, Radha Holla Bhar, Charles T. Mehlman

Bibliographic record

VenueJournal of Bioethical Inquiry · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
FundersUniversity of Texas MD Anderson Cancer CenterDebreceni EgyetemUniwersytet Śląski w KatowicachŚląski Uniwersytet Medyczny w KatowicachThompson Rivers University
KeywordsPublishingPublic relationsConflict of interestPeer reviewProcess (computing)SociologyQuality (philosophy)SubjectivityPolitical sciencePsychologyLawComputer scienceEpistemology

Abstract

fetched live from OpenAlex

Editors have increasing pressure as scholarly publishing tries to shore up trust and reassure academics and the public that traditional peer review is robust, fail-safe, and corrective. Hidden conflicts of interest (COIs) may skew the fairness of the publishing process because they could allow the status of personal or professional relationships to positively influence the outcome of peer review or reduce the processing period of this process. Not all authors have such privileged relationships. In academic journals, editors usually have very specialized skills and are selected as agents of trust, entrusted with the responsibility of serving as quality control gate-keepers during peer review. In many cases, editors form extensive networks, either with other professionals, industry, academic bodies, journals, or publishers. Such networks and relationships may influence their decisions or even their subjectivity towards a set of submitting authors, paper, or institute, ultimately influencing the peer review process. These positions and relationships are not simply aspects of a curriculum, they are potential COIs. Thus, on the editorial board of all academic journals, editors should carry a COI statement that reflects their past history, as well as actual relationships and positions that they have, as these may influence their editorial functions.

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)0.012
metaresearch head score (Gemma)0.161
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.486

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.161
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0030.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.1450.067

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 head0.809
GPT teacher head0.617
Teacher spread0.192 · 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
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

Citations40
Published2019
Admission routes1
Has abstractyes

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