Trust in Label Information to Recuperate Consumer's Confidence for Meat: A Compared Analysis among Canada, Italy, and Spain
Bibliographic record
Abstract
The analysis consisted in a field experiment performed by testing the opinions of different consumers groups: two of them located in the European Regions respectively: Friuli-Venezia-Giulia in Italy and Navarra in Spain; a third one located in the Ontario region, Canada. The data were processed by using a multivariate structural equation in the multi-group version. The enquiry was performed in 2003, just after the news about BSE disease were released in some UE countries, and consisted in a regional survey designed ad' hoc and submitted by face to face home made interviews, to collect information about consumer habits, opinions and evidences about trust in the food label. By testing the cross-country consumer's behaviours it was allowed to get evidences of the customers confidence (credence quality), with the information diffused by different markets outlets and in different regions. The conclusions were that the market channels released different amount of information about the risk safety specifically: i) at the hypermarket the information were passed to consumers through the product label (objective trait); ii) at the traditional butcher's it prevailed the trust in the vendor that generated the credence quality in the product (psychological trait). These different consumer's attitudes were translated into behavioural attitudes and decisions to buy the beef meat product.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".