Bibliographic record
Abstract
Public health scientists should be aware of the motives of research sponsors and their potential impact on health Concern over commercial sponsorship of medical research is at an all time high these days. As academic medical schools become increasingly dependent on financial relationships with the pharmaceutical industry, for example, there have been calls for more stringent standards for research contracts and public disclosure of potential conflicts of interest.1 But, so far, severing industry ties completely has not been considered as a serious option. The case with tobacco, however, is different. A small but growing number of academic institutions (most recently the Harvard School of Public Health and the Arizona College of Public Health) have approved official policies prohibiting their faculty from receiving financial support from tobacco companies and their affiliates. Some prominent funding agencies have also taken a stand. The Wellcome Trust, the American Legacy Foundation, the Public Health Association of Australia, and the National Cancer Institute of Canada will not fund researchers who concurrently receive tobacco industry funding or support. Cancer Research UK is currently considering adopting a similar policy. Indeed, the tobacco industry is fundamentally different from, say, the chemical or pharmaceutical industries. While Big Tobacco does not have a monopoly on impure science, it is the undeniable leader in organised subterfuge and manipulation of the scientific process. Over half a century, the industry has used quasi-scientific organisations, such as …
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.069 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".