Context and capacity: The potential for performance-based agricultural water quality policy
Bibliographic record
Abstract
Current Canadian policy approaches to agricultural water quality encourage the adoption of best management practices through voluntary, incentive-based measures. Despite these measures, concerns about agricultural impacts on water quality persist. Performance-based policy approaches with incentives that are tied to defined outcomes, and not to particular practices, may have an important role in managing water quality. Five performance-based approaches to address water quality in agricultural landscapes were identified: water quality trading/permitting, differentiated payments for ecosystem services, reverse auctions; emissions charges, and cross-compliance (a hybrid measure). The purpose of this paper is to critically assess the institutional and socio-cultural context that facilitated existing performance-based policy instrument adoption. Through this analysis, three key contextual factors were identified as enablers of performance based approaches: (1) social context, (2) institutional capacity and (3) standardized, consistent and robust estimation methodologies. A framework was developed to classify performance based programs and approaches. The application of the findings from this research and the classification framework provide an organized approach to assess the feasibility of implementing performance-based approaches for agri-environmental water quality policy.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".