Importance of traceability for sustainable production: a cross‐country comparison
Bibliographic record
Abstract
Abstract Traceability systems are becoming an important tool for tracking, monitoring and managing product flows through food supply chains. Traceability can be used as a method of certifying production, processing and nutritional credence attributes of food products. Environmentally sustainable production is a credence attribute that is gaining in importance in the eyes of consumers. In this research, the importance of traceability in verifying environmentally sustainable production practices is examined. The data were collected in online surveys related to consumer's perceptions and concerns about food safety, trust and reported behaviour related to meat consumption in three countries – Canada, the US and Japan. Determinants of traceability in verifying environmentally sustainable production practices include respondents' locus of control about food safety, food purchasing characteristics such as whether they normally buy organic products or shop at supermarkets and general traits such as worry and trust. In comparing across countries, there are significant differences in the interests in traceability to verify environmentally sustainable production practices and in the determinants of level of importance ascribed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".