Ex-ante analysis of structural change adjustments under the canadian agricultural income stabilization program
Bibliographic record
Abstract
The Canadian Agricultural Income Stabilization (CAIS) program was the new farm income support program introduced by the Canadian government in 2004. CAIS was designed to stabilize farm income by covering large and small declines in a reference margin. The reference margin is based on a five-year (Olympic) average of the production margin (defined as revenue minus expenses). Payouts are made if the production margin for the current year is less than for the reference margin. Given the nature of how reference margins are calculated, scope exists for producers to alter aspects of their production in order to become eligible for a payout. Such structural change is problematic as it suggests scope for misuse of the program (i.e., moral hazard). To deter potential moral hazard, CAIS includes a structural adjustment mechanism (SAM) to prevent such misuse. This study determines which factors within and outside the control of producers affects the reference margin, and the degree and direction of effect. An econometric model was used to estimate the impact of producer and market factors associated with structural adjustment on producers' reference margins. These results were used to differentiate between actual structural adjustment and variation based on fluctuations in different factors.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".