How do U.S. and Canadian consumers value credence attributes associated with beef labels after the North American BSE crisis of 2003?
Bibliographic record
Abstract
Abstract A consumer survey conducted in 2006 ( n = 419), and therefore after the first confirmed bovine spongiform encephalopathy (BSE) cases in North America in 2003, employs attribute‐based choice experiments for a cross‐country comparison of consumers' valuation of credence attributes associated with beef steak labels; specifically a guarantee that beef was tested for BSE, a guarantee that the steaks were produced without genetically modified organisms (GMOs) and a guarantee that beef steaks were produced without growth hormones and antibiotics. Considering consumers' socio‐economic characteristics, the results suggest that consumers in Montana (U.S.) and Alberta (Canada) are significantly heterogeneous in their valuation of the above attributes, although consumers' relative valuation of these process attributes does not appear to have changed since the 2003 BSE crisis in each region. Alberta consumers place a significant valuation on beef tested for BSE, which is striking because Canada's current legal environment does not permit testing and labelling of such beef by private industry participants. Montana consumers' valuation was found highest for a guarantee that the steaks were produced without GMO. Effective supply‐chain responses to consumers' valuation of credence attributes, for example, in the form of labelling, should therefore take consumers' heterogeneity into account.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".