How Coca-Cola Shaped the International Congress on Physical Activity and Public Health: An Analysis of Email Exchanges between 2012 and 2014
Bibliographic record
Abstract
There is currently limited direct evidence of how sponsorship of scientific conferences fits within the food industry's strategy to shape public policy and opinion in its favour. This paper provides an analysis of emails between a vice-president of The Coca-Cola Company (Coke) and prominent public health figures in relation to the 2012 and 2014 International Congresses of Physical Activity and Public Health (ICPAPH). Contrary to Coke's prepared public statements, the findings show that Coke deliberated with its sponsored researchers on topics to present at ICPAPH in an effort to shift blame for the rising incidence of obesity and diet-related diseases away from its products onto physical activity and individual choice. The emails also show how Coke used ICPAPH to promote its front groups and sponsored research networks and foster relationships with public health leaders in order to use their authority to deliver Coke's message. The study questions whether current protocols about food industry sponsorship of scientific conferences are adequate to safeguard public health interests from corporate influence. A safer approach could be to apply the same provisions that are stipulated in the Framework Convention on Tobacco Control on eliminating all tobacco industry sponsorship to the food industry.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".