Business Risk Management Program and risk‐balancing in Ontario hog sector: An empirical analysis
Bibliographic record
Abstract
Abstract Business risk management (BRM) has been an important focus of Canadian agricultural policy in the New Millennium. Safety net payments received by farmers can alter their investment portfolio and lead to risk‐balancing behavior in agriculture. Risk‐balancing is an unintended consequence of the farm safety net program and has a direct implication for future growth and sustainability of farm business. Does risk‐balancing exist in Ontario agriculture? This question is addressed in this paper using data for the hog sector in Ontario. While safety net programs were designed to address Business Risk (BR) for all farms, our empirical results indicate that CAIS/AgriStability payments reduced BR for small, medium, and large farms. The results from our fixed effect panel regression analysis demonstrate that there is a significant risk‐balancing behavior among medium hog farms in Ontario. Our results also reveal that the presence of risk‐balancing behavior in Ontario hog sector does not pose any problem for future growth of the hog sector or the long‐term sustainability of the farm safety net program.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".