Addressing Behavior and Policy Around Meat: Associating Factory Farming With Animal Cruelty “Works” Better Than Zoonotic Disease
Bibliographic record
Abstract
Research on shifting attitudes or behaviors surrounding the use of animal products traditionally focuses on animal cruelty. How this approach may differ from exposure on the zoonotic disease transmission risk factory farms pose is unclear. The present study sought to examine how information regarding zoonotic disease may stimulate concern for animals/concern for human health, respectively, and thus predict lower willingness to consume meat, when compared with animal cruelty and a control condition. The extent to which such information could shift support for changing conditions on factory farms was also examined. In a preregistered experiment (n = 454), participants were exposed to an informative paragraph on either (a) zoonotic disease transmission risk from factory farming, (b) animal cruelty on factory farms, or (c) a control paragraph. Those in the animal-cruelty condition were significantly more likely to indicate lower meat consumption willingness and higher support for changing conditions on factory farms, when compared with the two other conditions. Concern for animal health and welfare mediated the relationship between the combined experimental conditions and both dependent variables, when compared with the control condition. Upon examining the moderating role of human supremacy beliefs (HSB), a conditional effect was found across all conditions, with higher HSB predicting higher meat consumption willingness and lower support for changing conditions on factory farms. This study offers evidence for the intervention potential of informative excerpts. These findings also emphasize animal cruelty as a more effective way to mobilize support for behaviors and policies aimed at reducing animal-product consumption.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".