The Effect of Consumers’ Trust on Stated Responses to Food Safety Incidents: Case of Meat in Canada and the U.S
Bibliographic record
Abstract
In this study the effect of consumers’ trust in agencies on risk perceptions, risk attitudes and selfreported responses to food safety events is assessed. The particular focus is on how trust has influenced self-reported reductions in consumption of beef in the U.S. and Canada, and pork in Canada arising from food safety concerns. Data for this analysis was collected through online surveys and was analysed using tobit and probit models. Results show that general trust significantly influences risk perceptions and risk attitudes about beef and pork consumption in Canada and beef consumption in the U.S. Although there are some variations in results, trust in agencies significantly influence risk perceptions, risk attitudes and reductions in consumption of beef and pork in Canada and beef in the U.S. Compared to males, female respondents have higher risk perceptions and lower risk attitudes about beef and pork in Canada and beef in the U.S. and were more likely to reduce their pork consumption in Canada and beef in the U.S. Respondents that recalled a food safety event had higher risk perceptions and significantly reduced their meat consumption in both countries. Respondents that perceived the consumption of beef and pork as being risky reported that they had significantly reduced their consumption of these meats while those that were more willing to accept the risks of consuming these meats reported that they had not changed their consumption levels. Results suggest that risk perceptions, risk attitudes and trust play significant roles in terms of influencing consumer response to food safety concerns. The occurrence of food safety incidents in one period can influence consumer responses to later food safety incidents through the effect on changing risk perceptions, risk attitudes and trust in the food system.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".