Traceability in the Canadian Red Meat Sector: Do Consumers Care?
Bibliographic record
Abstract
Increased traceability of food and food ingredients through the agri‐food chain has featured in recent industry initiatives in the Canadian livestock sector and is an important facet of the new Canadian Agricultural Policy Framework (APF). While traceability is usually implicitly associated with ensuring food safety and delivering quality assurances, there has been very little economic analysis of the functions of traceability systems and the value that consumers place on traceability assurances. This paper examines the economic incentives for implementing traceability systems in the meat and livestock sector. Experimental auctions are used to assess the willingness to pay of Canadian consumers for a traceability assurance, a food safety assurance, and an on‐farm production method assurance for beef and pork products. Results from these laboratory market experiments provide insights into the relative value for Canadian consumers of traceability and quality assurances. Traceability, in the absence of quality verification, is of limited value to individual consumers. Bundling traceability with quality assurances has the potential to deliver more value.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".