Conflicts of interest in e‐cigarette research: A public good and public interest perspective
Bibliographic record
Abstract
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.
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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 | 0.257 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.085 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.078 | 0.045 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".