Influence of generalized trust on Canadian consumers’ reactions to the perceived food risk of three recurring BSE cases
Bibliographic record
Abstract
Interest in the influence of trust on consumers’ responses to food risk perceptions associated with Canadian instances of BSE motivates this study, in which Canadian households’ expenditures on fresh meat are assessed in the context of the first three recurring risk events in which bovine spongiform encephalopathy (BSE) was found to have affected Canadian cows. Engel Curve analysis focusing on the dynamics of the monthly meat expenditure shares for a selected sample of 437 Canadian households for 2002 through 2005 is applied based on data on household expenditures for meat purchased by a national sample of Canadian households from the Nielsen Homescan® Canadian panel, supplemented by survey responses on BSE risk perceptions and measures of trust. Two sets of models are estimated: Engel curves in differences with instruments in levels and Engel curves in levels with instruments in differences. It is found that habit persistence limited households’ reductions of beef purchases following the first BSE event and that that trust limited households’ reduction in beef expenditure shares following the subsequent two BSE cases. Significant seasonal effects and a significant negative influence on beef expenditure shares are also found, consistent with the trend of declining consumption of beef in Canada since the late 1990s.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".