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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".