A Framework to Evaluate Wildlife Feeding in Research, Wildlife Management, Tourism and Recreation
Bibliographic record
Abstract
Feeding of wildlife occurs in the context of research, wildlife management, tourism and in opportunistic ways. A review of examples shows that although feeding is often motivated by good intentions, it can lead to problems of public safety and conservation and be detrimental to the welfare of the animals. Examples from British Columbia illustrate the problems (nuisance animal activity, public safety risk) and consequences (culling, translocation) that often arise from uncontrolled feeding. Three features of wildlife feeding can be distinguished: the feasibility of control, the effects on conservation and the effects on animal welfare. An evaluative framework incorporating these three features was applied to examples of feeding from the literature. The cases of feeding for research and management purposes were generally found to be acceptable, while cases of feeding for tourism or opportunistic feeding were generally unacceptable. The framework should allow managers and policy-makers to distinguish acceptable from unacceptable forms of wildlife feeding as a basis for policy, public education and enforcement. Many harmful forms of wildlife feeding seem unlikely to change until they come to be seen as socially unacceptable.
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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 | 0.136 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".