Sports and sustainable development: the troubling absence of meat sourcing policies in the sports sector
Bibliographic record
Abstract
The excessive use of antibiotics in industrial meat production in the U.S. incurs severe health implications for animals, humans, and the environment, thereby threatening the integrated health of the ecosystem and sustainable development. While the consumption of meat, including hot dogs, chicken wings, and hamburgers, is a hallmark of attending professional sports events in North America, the sourcing policies for meat in the realm of professional sports remain relatively obscure. We conducted a content analysis case study on the four major sports leagues in North America, their teams and stadium practices. Our objective was twofold: first, to investigate existing sustainability initiatives at the league, team, and stadium levels; and second, to examine whether there are any food sourcing programs, specifically meat sourcing policies that might encourage the consumption of meat produced without the use of antibiotics, in the sports sector that are designed to mitigate ecological ramifications of meat consumption within sports contexts. Results show that existing sustainability initiatives at the three levels are focused primarily on reducing carbon emissions and waste. There is, however, a notable neglect of food sourcing policies, which is concerning given that industrial animal agriculture is a leading cause of antibiotic resistance and environmental degradation. This suggests that meat sourcing policy is a missing piece in current sustainability initiatives. The major sports leagues should therefore consider incorporating pertinent policies, such as procuring meat-based products produced without the use of antibiotics to help strengthen their existing efforts in achieving their sustainable development goals.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".