Effect of Contract Attribute Levels on Willingness to Participate in a Working Wetlands Program
Bibliographic record
Abstract
Wetlands are an integral part of duck habitat in the Prairie Pothole Region, which covers three Canadian provinces and five U.S. states. They often overlap with cropland, creating issues for farmers. A program that provided funding to farmers who agree to not alter wetlands and continue to farm the land was introduced in North Dakota called the Working Wetlands Program. After four years, participating farmers were surveyed. A choice experiment was used to investigate the effect of program contract attributes including payment, length, and whether the contract is binding, on willingness to enroll. A random parameters logit model was estimated. Nonbinding contracts are preferred to binding regardless of other attributes. If it is important that the contract be binding, notable for policymakers is that shorter lengths have a higher participation rate than longer lengths. This information is valuable to policymakers as they continue to build a national program.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".