Cost-sharing Incentive Programs for Source Water Protection: The Grand River’s Rural Water Quality Program
Bibliographic record
Abstract
Canadian provinces have become increasingly concerned with possible contamination of water from upstream agricultural activities. Many see watershed-based source protection, so called "source-to-tap" programs, as a means of improving water quality. A key factor in the success of these programs is the extent to which they provide incentives to farmers to undertake actions that ultimately result in a reduction of non-point source pollution. One type of program is cost-sharing whereby farmers are reimbursed for out-of-pocket expenses relating to best management practices which are expected to reduce runoff into water courses. Given increasing reliance on these types of programs, it is necessary from a public policy perspective to identify design features leading to the greatest likelihood of farmer participation. This paper examines Ontario’s Rural Water Quality Program for the Grand River using data from the first seven years of its operation, along with data from Agricultural Canada’s Farm Census, to model and estimate participation rates. Significantly positive determinants include: the maximum grant available and performance incentives, although both with diminishing returns. Projects with a one-time capital subsidy alone are much less likely to encourage participation than projects that combine a subsidy with a performance incentive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".