Entangling Conservation Schemes and Its Effects on Farmers’ Participation: The Case of Two Agri-environmental Incentives in Quebec
Bibliographic record
Abstract
"Incentive-based mechanisms, such as payments for ecosystem services (PES) are increasingly being employed to encourage adoption of biodiversity conservation practices for the provision of multiple ecosystem services and the preservation of agricultural commons. PES are not created in an institutional vacuum, and their success in encouraging participation might depend on their interactions with previous programs and schemes. This paper analyzes how the institutional characteristics and interactions of incentive-based mechanisms influence farmers’ participation and therefore the achievement of desired socio-ecological outcomes, This research pays close attention to the institutional framework of two programs in the province of Québec, Canada: the Prime-Vert Program (a public agri-environment scheme) and the ‘Alternative Land Use Services’ (ALUS) initiative (a privately-funded 'PES' scheme). The institutional prescriptions of these two programs were examined and compared through the lenses of the Institutional analysis and development (IAD) framework, suggested by Ostrom (2005). Moreover, this work discusses the impact of the functional characteristics described by the IAD framework on farmers’ participation by analyzing the level of farmers’ engagement in the implementation and management of agri-environmental schemes (Prager and Freese 2009). The institutional comparison of these two incentives showed a strong dependence of the private PES on the public scheme, rendering both programs ultimately managed under the remit of the provincial government. While, the integration of both programs could help diversify sources of funding for farmers, the multiplicity of rules which govern the integration of these two programs tend to treat farmers as passive beneficiaries to a network of centralized subsidies."
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".