Governance of risk management programs: Learning from Québec's Farm Income Stabilization Insurance program
Bibliographic record
Abstract
Involving stakeholders in program decision-making can support existing programs and reduce tensions against the state. However, to be involved, stakeholders may request specific mechanisms to influence program design or outcomes. This paper analyzes the design of four consultation mechanisms and the resultant stakeholder experiences in a provincial Canadian program called Farm Income Stabilization Insurance (FISI). The program offers a protection against low prices. The findings of this paper are based on 18 semi-structured interviews conducted with current and former participants familiar with the mechanisms. An analysis is accomplished through Arnstein’s ladder of citizen participation and Glasser’s choice theory. Results show that stakeholder representation can be improved by adequately designing consultation mechanisms and implementing specific actions. Recommended practices include separating political and technical discussions, asking a third party to take charge of the consultation mechanisms and prepare information, formally laying down recurrent mechanisms, and involving high-ranking individuals in the discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".