The Extent and Nature of Food and Beverage Company Sponsorship of Children’s Sports Clubs in Canada: A Pilot Study
Bibliographic record
Abstract
Food and beverage marketing is considered a determinant of childhood obesity. Sponsorship is a marketing technique used by the food industry to target young people when they are engaged in sports. The purpose of this study was to document the frequency and nature of food company sponsorship of children's sports clubs in Ottawa, Canada. Using national data on sports participation, the five most popular sports among Canadian children aged 4-15 years were first selected for inclusion in the study and relevant sports clubs located in Ottawa (Canada) were then identified. Sports club websites were reviewed between September and December 2018 for evidence of club sponsorship. Food company sponsors were identified and classified by food category. Of the 67 sports clubs identified, 40% received some form of food company sponsorship. Overall, sports clubs had 312 commercial and noncommercial sponsors. Food companies constituted 16% of total sponsors and were the second most frequent type of sponsor after sports-related goods, services, and retailers (25%). Fast food restaurants and other restaurants accounted for 45% and 41% of food company sponsors, respectively. Food company sponsorship of children's sports clubs is frequent with some promoting companies or brands associated with unhealthy foods. Policymakers should consider restricting the sponsorship of children's sports clubs by food companies that largely sell or promote unhealthy foods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".