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Record W2970765139 · doi:10.1111/bioe.12619

Conflicts of interest in e‐cigarette research: A public good and public interest perspective

2019· article· en· W2970765139 on OpenAlexaff
Benjamin Capps, Yvette van der Eijk, Timothy Krahn

Bibliographic record

VenueBioethics · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPublic interestPerspective (graphical)Conflict of interestGeneral interestPublic relationsPolitical scienceSociologyPsychologyEpistemologyLawComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

The tobacco industry's involvement in the electronic cigarette research that informs public health policy is controversial. On the one hand, some are concerned that their involvement presents conflicts of interest that bias research outputs and invalidate the policies that use them. On the other hand, some have argued that the tobacco industry may support valid research and contribute to the goals of public health, for instance, if the interests of the e-cigarette industry could be part of a tobacco smoking cessation policy. We approach this debate from the ethical perspective of the public interest and the public good, considering how legitimate researchers can square their expert opinion with validating tobacco industry-funded research, given the perfidy of the tobacco industry and paucity of robust, conclusive evidence on the public health impacts of liberalizing e-cigarette use.

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)0.257
metaresearch head score (Gemma)0.306
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2570.306
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.004
Science and technology studies0.0110.085
Scholarly communication0.0320.023
Open science0.0070.014
Research integrity0.0780.045
Insufficient payload (model declined to judge)0.0070.001

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 head0.922
GPT teacher head0.645
Teacher spread0.276 · 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 designTheoretical or conceptual
DomainEvaluation
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

Citations6
Published2019
Admission routes1
Has abstractyes

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