Corporations and Health: The Need to Combine Forces to Improve Population Health
Bibliographic record
Abstract
The recent concerns raised about commercial determinants of health (CDoH) are not new. Numerous organizations around the world are working on these issues. These groups have emerged in response to specific issues and contexts and bring with them a diversity of interests, worldviews and strategies for change. In creating the 'Governance, Ethics and Conflicts of Interest in Public Health' network in 2018, our hope was to broaden our engagement with other actors advocating for change and strengthen our collective efforts. For academics, this requires moving further beyond the collective comfort zone of peer-reviewed publications, working with the media and those with political expertise, and learning from and supporting other stakeholders with a common vision.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".