Consumer's Stated Trust in the Food Industry and Meat Purchases
Bibliographic record
Abstract
Research indicates that consumers are particularly concerned about the safety of meat. More highly processed meat is perceived as more unsafe than fresh or natural meats, i.e., consumers trust processed meat less. This paper studies the relationship between perceived trust and day-to-day purchase behavior for meat, giving special attention to the degree of meat processing. Controlling for trust in food chain actors and demographic and socio-economic variables, actual meat purchases of Canadian households are linked to answers from a commissioned food attitudes survey completed by the same households. Expenditures for processed and total meat (but not for fresh meat) are significantly different by three levels of trust in the food industry. Consumer with the lowest trust levels consume less (especially of processed meat) compared to those with higher trust levels. However, in a multivariate setting, trust shows no effect on fresh or processed meat purchases with or without demographic and socio-economic control variables, suggesting that the impact of trust on meat purchases is only small. However, the low trusting consumer segment could potentially be a target for marketing strategies focused on reputation and quality to increase sales in this particular group.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".