Impacts of beef producer compensation programmes to remediate negative economic outcomes of bovine spongiform encephalopathy in Canada
Bibliographic record
Abstract
Before the first domestic case of bovine spongiform encephalopathy (BSE) was identified in May 2003, Canada was the world's third largest exporter of cattle behind the USA and Australia. After the detection of BSE, over 30 countries imposed an immediate ban on imported Canadian beef and cattle products, including the USA. The interdependence of the Canadian beef industry with that of the US market was a critical reason in Canada's market vulnerability. Reopening of the US border was prolonged and beef producers adopted various strategies to deal with the loss of income. Measures taken by individual farmers were not sufficient in supplementing their loss of income, thus creating a need for government funding and support programmes. A comparison of economic impacts and analysis of existing literature shows subsidy programmes did little to restore long-term stability or reduce market vulnerability to Canadian farm producers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".