Do financial incentives motivate conservation on private land?
Bibliographic record
Abstract
Abstract Financial incentives may aid in conservation if they broaden the numbers and types of landowners who engage in protection and conservation management on private land. We examined the hypotheses that financial incentives (1) encourage participation of people with lower autonomous motivation towards conservation and lower self-transcendence (i.e. benevolence and universalism) values compared to participants in similar programmes without such incentives; (2) enable more on-ground works and activities; and (3) enhance feelings of competence and autonomy with respect to conservation actions. We surveyed 193 landowners in private land conservation programmes in Tasmania, only some of whom had received financial incentives. All of these landowners had high self-transcendence values, and autonomous motivation towards the environment. Owners of large properties and participants with higher self-enhancement values, lower self-transcendence values and lower autonomous motivation towards the environment were slightly more likely to engage in incentive programmes. However, people who received funding did not report more conservation actions than people in programmes without incentives. Owners of larger properties receiving incentives reported fewer conservation actions. Thus financial incentives probably recruited a few into nature conservation who may not have otherwise engaged, but did not result in a more intensive level of conservation management. Our results caution against the blanket-use of incentives amongst landowners who may already have values and motivations consistent with environmental action, and point to the need for further research on the socio-psychological characteristics of landowners, to examine the contextual factors that influence the effects of conservation payments.
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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 | 0.003 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".