USING LINKED HOUSEHOLD-LEVEL DATASETS TO EXPLAIN CONSUMER RESPONSE TO BSE IN CANADA
Bibliographic record
Abstract
Household-level Canadian meat purchases from 2002-2008 and the Food Opinion Survey conducted in 2008 were used to explore consumer responses to Bovine Spongiform Encephalopathy (BSE) at the national level in Canada. Three measures of beef purchased were used to understand consumers‟ reaction under food risk. A random effects Logit model was applied to test whether any beef was purchased during a given month. Consumption in terms of unit purchases was measured with a random effects Negative Binomial model and consumption in terms of beef expenditure was measured with a standard random effects model. In this study, household heterogeneity in actual 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 in the study, with consumers in eastern Canada reacting most negatively to BSE. Consumers were less likely to reduce beef purchases during BSE events when they believed food system decision makers were honest, as opposed to knowledgeable, about food safety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.010 |
| 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".