RESEARCH AND MANAGEMENT VIEWPOINT: WHY COMPENSATING WILDLIFE DAMAGES MAY BE BAD FOR CONSERVATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an effort to attenuate human–wildlife conflict and promote conservation of charismatic megafauna, compensation programs for wildlife damages have been implemented in many countries. Compensating pastoralists and farmers for damage caused by wildlife reduces hunting pressure on wild animal populations. However, it can also lead to a decrease in efforts to prevent damage and exacerbate conflicts with wildlife. Furthermore, compensation programs increase the return to agriculture and can therefore be viewed as a subsidy toward crop and livestock production. Such subsidies can trigger agricultural expansion (and habitat conversion), an inflow of agriculture producers, and intensification of agricultural production. Each of these impacts is shown to have potentially adverse effects on the wildlife population that compensation intends to favor. In some circumstances, the net effect on the wildlife stock could be negative. This calls for a careful assessment of local ecological and economic conditions before compensation is implemented. Incentive mechanisms that are directly tied to conservation outcomes (e.g., payments to locals based on the size of the wildlife population) should be considered instead of compensation programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it