Canadian Business Risk Management: Private Firms, Crown Corporations, and Public Institutions
Bibliographic record
Abstract
Abstract This paper considers the current and possible institutions (programs, policies, participants, etc.) that govern public Business Risk Management (BRM) in Canada. This is an important policy topic for two reasons: BRM spending accounts for the vast majority of public monies funneled to Canadian agricultural producers and the upcoming Canadian Agricultural Partnership includes a mandated BRM review and thus presents an opportunity to change these institutions in meaningful ways. We pay particular attention to the rhetoric surrounding greater involvement of private insurance, the lack of rhetoric regarding the use of crown corporations, and issues of subsidization. We conclude with policy recommendations favoring commodity‐specific revenue versus whole‐farm net margin insurance, a possible reduction in subsidy levels, and a call to reconsider the role of crown corporations. We also make programming recommendations regarding the discontinued use of private reinsurance, a reduction in the level of program reserves, and greater transparency. Ce document examine les institutions actuelles et potentielles (programmes, politiques, participants, etc.) qui gouvernent la gestion des risques d'entreprises (GRE) au Canada. Deux raisons rendent important ce sujet politique : les dépenses liées á la GRE totalisent la grande majorité des fonds publics accordés aux producteurs agricoles, et le futur cadre politique agricole offre l'occasion de modifier ces institutions de manière significative. Nous portons une attention particulière á la rhétorique entourant l'implication plus soutenue des assurances privées, á l'absence de rhétorique au sujet de l'utilisation de sociétés d'État, et aux enjeux de subvention. Nous concluons avec des recommandations politiques favorisant les assurances de revenus provenant spécifiquement de produits agricoles versus celles de la marge nette de l'exploitation globale, une potentielle réduction des niveaux de subvention, et un appel á tenir compte du rôle des sociétés d'État. Nous proposons aussi des recommandations de programmation concernant l'arrêt de l'utilisation des réassurances privées, une réduction du niveau des programmes de réserve, et une transparence accrue.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".