Incentive Structure of and Private Landowner Participation in an Endangered Species Conservation Program
Bibliographic record
Abstract
In the United States, voluntary incentive programs that aid conservation of plant and wildlife species on private lands provide a structural solution to the problem of protecting endangered species by reducing costs and enhancing benefits to landowners. We explored the potential for incentives to encourage landowners to manage land cover for the benefit of endangered songbirds in central Texas (U.S.A.) by asking landowners to indicate their preferences for financial incentives, technical assistance, and regulatory assurances. We identified owners of potential songbird habitat and collected data in face-to-face interviews and self-administered questionnaires. We used a latent-class stated-choice model to identify 3 classes of landowners whose choices varied on the basis of their attitudes and perceived social norms: (1) strong positive attitude, perceived social pressure to participate, and willing to participate with relatively few incentives, (2) weak positive attitude, perceived no social pressure to participate, and required strongest incentives, and (3) negative attitude, perceived social pressure not to enroll, and unwilling to participate regardless of incentive structure. Given this heterogeneity in preferences, conservation incentives may increase management of land cover to benefit endangered species on private lands to some degree; however, exclusive reliance on incentives may be insufficient. Promoting conservation on private lands may be enhanced by integrating incentives into an approach that incorporates other strategies for conservation, including social networks and collaborative processes that reinforce social norms.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".