A Comparative Analysis of US and Canadian Consumers' Perceptions Towards BSE Testing and the use of GM Organisms in Beef Production: Evidence from a Choice Experiment
Bibliographic record
Abstract
Since the discovery of the first BSE case in North America in 2003, food safety has become a major issue to policymakers and consumers alike. In both Canada and the US, governments and industry have responded with a variety of quality assurance, traceability and labeling schemes. However, there is little information available on the extent to which consumer perceptions differ regionally across North America towards labeling schemes. This paper attempts to fill this gap, by providing results on a variety of beef labeling strategies from choice experiments that were conducted in Alberta (Canada) and Montana (US). The analysis focuses on consumers' perceptions towards negative voluntary labeling with regard to BSE testing, genetically modified organisms (GMO) and the use of growth hormones in beef production. We find that four years after the first BSE case emerged in North America, consumers are willing to pay most to avoid risks associated with BSE. US and Canadian consumers are found not to be significantly heterogeneous in their preferences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".