Food QUALITY VERIFICATION: WHO DO CONSUMERS TRUST?
Bibliographic record
Abstract
Food markets are increasingly characterized by an array of quality assurances with respect to credence attributes, many of which relate to agricultural production methods. A variety of organizations are associated with these quality assurance claims, including private, third party and public sector organizations. How do quality verifications from different sources affect consumer food choices? Who do consumers trust for assurances about credence attributes? This paper draws upon two recent studies to explore Canadian consumer attitudes toward environmental quality claims in a bread product and animal welfare quality claims in a pork product, along with attitudes toward quality verification from different sources. Analysis from two discrete choice experiments is presented, with latent class models used to explore heterogeneity in consumer preferences. The key message from both studies is the importance of considering heterogeneity in consumer preferences when examining attitudes toward quality verification. Both studies reveal distinct segments of consumers who have a high level of trust in verification by public sector agencies (government). In general, it was the respondents who exhibited the strongest preferences for the quality attributes who also tended to value public sector verification. Both sets of results also reveal a sub-set of consumers who tend to trust farmers, while both also reveal a clear segment of Canadian consumers who might be considered ‘conventional food’ consumers, with little interest in these quality attributes. Suggestions for further research are provided.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".