Bibliographic record
Abstract
Empirical evidence shows the links between sweetened soft drinks1Brownell KD Farley T Willett WC et al.The public health and economic benefit of taxing sugar-sweetened beverages.N Eng J Med. 2009; 361: 1599-1605Crossref PubMed Scopus (551) Google Scholar, 2Vartanian LR Schwartz MB Brownell KD Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis.Am J Public Health. 2007; 97: 667-675Crossref PubMed Scopus (1321) Google Scholar or fast food3Prentice AM Jebb SA Fast foods, energy density ad obesity: a possible mechanistic link.Obes Rev. 2003; 4: 187-194Crossref PubMed Scopus (503) Google Scholar and chronic illness such as obesity and diabetes. However, as a researcher investigating the causes of obesity, I have become disappointed at the presence of brands of unhealthy products at major conferences relating to obesity, physical activity, and nutrition. In recent years, brands such as Coca Cola Inc and McDonalds Corp have sponsored several conference events. At Obesity Week 2013—the annual scientific meeting of the Obesity Society—not only were Coca Cola products available, but the company also sponsored one of the keynote presentations. Meanwhile another keynote address highlighted the risks of consuming sweetened soft drinks. Last year Coca Cola Inc also sponsored the Congress of the European College of Sport Sciences in Amsterdam, Netherlands; the International Congress on Physical Activity and Public Health in Rio de Janeiro, Brazil; and the Childhood Obesity and Public Health Conference in Baton Rouge, LA, USA. Similarly, McDonalds Corp sponsored the California Dietetic Association Conference in Pomona, CA, USA, and both Coca Cola Inc and McDonalds Corp sponsored the Dietitians Association of Australia Conference in Brisbane, QLD, Australia, and the Canadian Obesity Summit in Vancouver, BC, Canada. It is time for academics and professionals to take a stand against these global companies, whose products have been linked with ill health, and for conference organisers to refrain from these conflicting and confusing partnerships. There is no place for brands of unhealthy consumption at health, physical activity, or nutrition conferences, and organising committees of such events should select sponsors that do not conflict with empirical evidence that might well be disseminated at the conference or in the journals they are aligned with. I declare no competing interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.100 | 0.010 |
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".