Business Risk Management Programs and Risk-balancing Behavior in Ontario Hog Sector
Bibliographic record
Abstract
Agri-food sectors in Canada are supported through safety net programs. CAIS/BRM programs were designed to help producers reduce BR by mitigating negative income shocks and reducing income variability. Nevertheless, according to the risk-balancing hypothesis, farms may take more FR in response to a reduction in BR as a result of program payments. If we find evidence of such behavior, risk-reduction efforts of CAIS/BRM programs may not generate intended outcomes. This thesis employs OFID tax-filing data over the 2003-2014 period to estimate the extent of risk-balancing in the Ontario hog sector as a result of AgriStability payments under CAIS/BRM. We find that that AgriStability payments were effective in reducing BR for medium and large farms but not small farms. Controlling for other determinants of financial risk, our log-log fixed-effects regression provides evidence of risk-balancing for medium and large farms in Ontario hog sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".