Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie
Bibliographic record
Abstract
Behavioral interventions, for example, through the use of incentives and information, are widely used by policymakers to induce welfare-improving behavior.When these interventions are used in specific contexts such as environmental conservation, individual knowledge, experience, and attitudes can lead to motivational crowding and may limit their efficacy.In this article, we investigate how incentives and information affect the behavior of recreational hunters in Alberta when tasked with curbing the spread of chronic wasting disease (CWD), a deadly wildlife disease.Embedded in a framed multiple-threshold public goods game, we test hunters' responses to various real-world policy instruments designed to increase hunting trips.We find that hunters take more trips (increase their contribution to the public good) in response to monetary incentives framed as extra hunting tags but do not respond to direct monetary rewards.These findings advance our knowledge of how incentives can lead to motivational crowding and provide meaningful policy guidance for wildlife management and other environmental programs.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.206 | 0.047 |
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".