Impact of BSE on Beef Purchases in Alberta and Ontario Quick‐Serve Restaurants
Bibliographic record
Abstract
This analysis employed a highly disaggregated household data set of Alberta and Ontario fast food purchases from May 2000 to May 2005. A double‐hurdle count data model allowed tests of the hypotheses that frequency of Bovine spongiform encephalopathy (BSE) media coverage affected neither a household's monthly probability of purchasing a beef entree, nor the household's monthly quantity of beef entrees purchased. Ontario consumers were more likely to stop purchasing beef entrees immediately following a surge in BSE media coverage, but those who did buy beef entrees maintained stable quantity levels. BSE media coverage did not systematically affect fast food purchases among Alberta consumers. Dans la présente analyse, nous avons utilisé un ensemble de données fortement désagrégées sur les achats de repas–minute effectués par des ménages de l'Alberta et de l'Ontario, entre mai 2000 et mai 2005. Un modèle de comptage à deux étapes (double hurdle count data model) a permis de vérifier les hypothèses selon lesquelles la concentration de la couverture médiatique de la crise de l'ESB n'influence pas la probabilité mensuelle d'un ménage d'acheter des repas–minute de bœuf ni la quantité mensuelle de repas–minute de bœuf achetés par un ménage. Les consommateurs ontariens étaient plus susceptibles de cesser d'acheter des repas–minute de bœuf immédiatement après une poussée de la couverture médiatique de la crise de l'ESB, mais ceux qui achetaient des repas–minute de bœuf ont maintenu les quantités achetées. La couverture médiatique de la crise de l'ESB n'a pas systématiquement influencé les achats de repas–minute chez les consommateurs albertains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".