Hog farmers' compliance and the role of agro-environmental institutions in the Missisquoi Bay
Bibliographic record
Abstract
Institutions, defined as rules, norms and strategies, play an important role in shaping socio-ecological interactions and determining human behavior. In Quebec, agro-environmental policies were introduced within the last 15 years to reduce the degrading effects of intensive agricultural production on land and water. However, the disturbance of water ecosystems remains a problem in agricultural areas in part due to producers' difficulties in complying with the present agro-environmental regulation. This research assesses the role of institutions in encouraging the compliance of hog farmers with Quebec's Règlement sur les exploitations agricoles (REA) in the Missiquoi Bay. This regulation is meant to reduce the ecological impact of farming systems and to guide animal production development in the province. This study follows the methodology of Crawford and Ostrom's (1995) "Grammar of Institutions" framework which identifies the actions encouraged, the actors assigned, the boundaries of the actions as well as the punitive measures present in any given institutional setting. Furthermore, this framework makes it possible to classify institution according to their grammatical content. This case study aligns with research conducted at the intercept of institutional and ecological economics, focusing on the role of institutions in fostering collective action and environmental outcomes as well as providing alternative policy recommendations according to normative foundations. The REA can be described by containing different types of institutions, classified by 84.5% of rules, 14.7% of norms and 0.8% of strategies. As most of the institutions in the REA exhibited complete grammar, the regulation can be considered to have a robust regulative role. However, when we analyzed rules according to the legitimacy of sanctions, we found out that the sanctions did not comply with all the conditions for their legitimacy, as the REA was not the result of collective action. The latter is a major parameter for the legitimacy and internalization of institutions. Our results show that although the REA looks rather robust from a grammatical perspective, it also offers a weak normative character. This study recommends the examination of other institutional design frameworks by focusing on the legitimacy of institutions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".