The Role of Consumer Risk Perceptions and Attitudes in Cross Cultural Beef Consumption Changes
Bibliographic record
Abstract
Beef food safety events have contributed to considerable market volatility, produced varied consumer reactions, created policy debates, sparked heated trade disputes, and generally contributed to beef industry frustrations. Better understanding of the forces causing observed consumer reactions in light of beef food safety events is critical for policy makers and industry participants. We examine whether consumers altered their beef consumption behavior because of their risk aversion and risk perceptions stemming from information about beef food safety in recent years. We use data from a total of 4, 000 consumers in the U.S., Canada, Mexico and Japan to estimate a two-stage Probit/double-bounded Tobit modeling framework. Results reveal there are stark differences in risk perceptions and risk aversion regarding beef food safety across consumers in the four countries and that these differences are revealed through different beef consumption behavior. An improved understanding of food safety perceptions and attitudes will enable policy makers and agricultural industries to better anticipate consumers changing consumption behavior, if a food safety event occurs. Consumers from the four countries examined exhibited heterogeneous food safety perceptions and attitudes. Results suggest that food safety management strategies should vary across countries because of identified differences in food safety risk attitudes and risk perceptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".