Using Linked Household-Level Data Sets to Explain Consumer Response to Bovine Spongiform Encepalopathy (BSE) in Canada
Bibliographic record
Abstract
Household-level Canadian meat purchases from 2002 to 2008 and a Food Opinions Survey conducted in 2008 were used to explore consumer responses to bovine spongiform encephalopathy (BSE) at the national level in Canada. Consumption in terms of the number of unit purchases was analyzed with a random-effects negative binomial model. In this study, household heterogeneity in meat purchases was partially explained using data from a self-reported food opinions survey. Of special interest was the hypothesis that consumers responded consistently to BSE in a one-time survey and in actual meat purchase behavior spanning years. Regional differences appeared, with consumers in eastern Canada reacting most negatively to BSE. Consumers responded more to the perception that food decision makers are honest about food safety than to the perception that they are knowledgeable, in maintaining beef purchases during BSE events.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".