A call to advance and translate research into policy on governance, ethics, and conflicts of interest in public health: the GECI-PH network
Bibliographic record
Abstract
Efforts to adopt public health policies that would limit the consumption of unhealthy commodities, such as tobacco, alcohol and ultra-processed food products, are often undermined by private sector actors whose profits depend on the sales of such products. There is ample evidence showing that these corporations not only try to influence public health policy; they also shape research, practice and public opinion. Globalization, trade and investment agreements, and privatization, amongst other factors, have facilitated the growing influence of private sector actors on public health at both national and global levels. Protecting and promoting public health from the undue influence of private sector actors is thus an urgent task. With this backdrop in mind, we launched the "Governance, Ethics, and Conflicts of Interest in Public Health" Network (GECI-PH Network) in 2018. Our network seeks to share, collate, promote and foster knowledge on governance, ethical, and conflicts of interest that arise in the interactions between private sectors actors and those in public health, and within multi-stakeholder mechanisms where dividing lines between different actors are often blurred. We call for strong guidance to address and manage the influence of private sector actors on public health policy, research and practice, and for dialogue on this important topic. Our network recently reached 119 members. Membership is diverse in composition and expertise, location, and institutions. We invite colleagues with a common interest to join our network.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".