Coca-Cola contracts could allow it to “quash” unfavourable research findings
Bibliographic record
Abstract
Coca-Cola might be able to suppress unfavourable findings from health research it funds at public universities in the United States and Canada, a new study has found.1 Researchers studied over 87 000 documents obtained through freedom of information requests and found several clauses that allow the drinks giant to terminate research projects without reason and walk away with the data. Coca-Cola funds research in the fields of nutrition, physical inactivity, and energy balance. It publishes a list of the funding it has allocated from 2010 to the present on its transparency website.2 The research team, from the University of Cambridge, London School of Hygiene and Tropical Medicine, the University of Bocconi, and non-profit group US …
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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 | 0.144 | 0.407 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.109 | 0.017 |
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".