How should we define, document, and prevent conflicts of interest in alcohol research?
Bibliographic record
Abstract
Babor, T. (2016). How should we define, document, and prevent conflicts of interest in alcohol research?. The International Journal Of Alcohol And Drug Research, 5(1), 5-7. doi:http://dx.doi.org/10.7895/ijadr.v5i1.232Aims: Conflicts of interest (COIs) in science and medicine have come under increasing scrutiny in recent years. This article reviews definitions of COI, as well as measures used to document, prevent, and manage COIs.Findings: The positive association between COIs and the outcomes of research has been documented in a substantial body of research covering a variety of fields, including addiction research. Attempts to address COIs include funding declarations, voluntary bans of receipt of industry funding, and ethical analyses.Conclusions: To protect the scientific integrity of the alcohol field from further influence from commercial and other competing interests, reasonable and consistent reporting procedures are needed at a minimum. Direct funding from major transnational alcohol producers involves major reputational and ethical risks that may require more stringent measures by professional societies, university administrators, and journal editors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.601 | 0.770 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.034 | 0.044 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.035 | 0.064 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".