Exploring motivation crowding around farmer incentives for riparian management in Nova Scotia
Bibliographic record
Abstract
Abstract Incentive programs to encourage landowners to protect habitat should be carefully designed to avoid motivation crowding: basically, replacing intrinsic reasons such as a land ethic with extrinsic ones like payments. Little research on motivation crowding tests real programs, and no such work has been done in Canada. We surveyed farmers in Nova Scotia in 2017 to explore whether participation in a new incentive program called Wood Turtle Strides, or knowledge about a similar incentive program potentially available in the future, would alter reported motivations to use riparian setbacks and buffers. Motivations to use setbacks or buffers were heavily intrinsic across all four survey cohorts: wildlife stewardship and sacrifice motivated actions more than social pressures. We were not able to statistically test for motivational crowding due to low program uptake and thus post‐program survey responses, but there was no evidence of second‐hand crowding: farmers being motivated by hearing about a program in an adjacent jurisdiction. Findings point to the significance of wildlife stewardship for many farmers, and persistent resistance to conservation among others, as well as a risk of low additionality. More post‐program research is necessary to fully understand the program's net impact on motivations and conservation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".