Decrypting the Processes of Policy Evolution: Comparing Québec and Ontario’s Evolution of Risk Management Policies
Bibliographic record
Abstract
Through policies or programs, states can influence industries to align with their vision of what a sector should become. However, this vision is subject to influence from actors of the industries which emphasize the importance of the relationship between the state and interest groups. In this dissertation, we observe the evolution of 9 Canadian agriculture risk management programs implemented by two provincial governments between 1990 and 2020 to understand how such programs coped with the new trends in agriculture. Through a review of parliamentary discussions and official documents, we evaluate the factors that justified program evolution based on the combination of the punctuated equilibrium and the social construction of target population theories. Our results show that risk management programs tend to align with the vision of agriculture carried out by the main interest groups. By doing so, new visions have a harder time to gain support and see programs being adapted to their reality. The long-lasting vision can then build on its accumulated political resources to direct support to similar farmers. Moreover, since these farmers are perceived as beneficial contributors to the society, they find governments generally favorable to their requests. This could then explain why risk management policies have seen their budgets greatly increased over the timeframe we covered as well as the multiplication of programs. On the other hand, the new visions in agriculture had a harder time to receive risk management programs adapted to their reality. Since most risk management policy discussions are realized through negotiations between the state and a dominant farming group, this group should ensure to include these new visions inside its discourse to maintain its legitimacy. Were the new visions to obtain enough attention by the state through other means, it could destabilize the polity and reduce the influence of farmers on agricultural policy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".