1. Economic Impacts of Bovine Spongiform Encephalopathy in Canada and Europe And The Effect of Compensation Programs
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 United States (U.S.) and Australia. After the BSE disclosure, the U.S. and 40 other countries imposed an immediate ban on imported Canadian beef and cattle products. The interdependence of the Canadian beef industry with that of the U.S. prior to the outbreak of BSE was a critical factor in Canada’s market vulnerability and the resulting economic impact. As the re-opening of the U.S. border was prolonged, beef producers adopted a variety of strategies to deal with the loss of income including refinancing existing loans and selling land or other assets. However these measures taken by individual farmers were not sufficient in completely supplementing their loss of income, thus creating a need for government funding and support. Little research has been done to assess the impact of government subsidies as a tool to offset the economic losses incurred by BSE. The analysis of the impacts of BSE and the resulting subsidies is extended to Britain, France, Germany and the European Union to see if government subsidies had a similar impact as compared to Canada. Analysis of existing literature shows the economic impacts to be heavily impacted by the structure of the beef industry and the subsidies to be impacted by consumption levels. The result of the subsidies is unclear; however due to lack of recent information the full analysis of the result of subsidy programs is difficult to determine.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".