Rating harms to wildlife: a survey showing convergence between conservation and animal welfare views
Bibliographic record
Abstract
Abstract Human activities may cause conservation concerns when animal populations or ecosystems are harmed and animal welfare concerns when individuals are harmed. In general, people are concerned with one or the other, as the concepts may be regarded as separate or even at odds. An online purposive survey of 339 British Columbians explored differences between groups that varied by gender, residency, wildlife engagement level and value orientation (conservation-oriented or animal welfare-oriented), to see how they rated the level of harm to wildlife caused by different human activities. Women, urban residents, those with low wildlife engagement, and welfare-orientated participants generally scored activities as more harmful than their counterparts, but all groups were very similar in their rankings. Activities that destroy or alter habitat (urban development, pollution, resource development and agriculture) were rated consistently as most harmful by all groups, including the most conservation-oriented and the most welfare-oriented. Where such a high level of agreement exists, wildlife managers should be able to design management actions that will address both conservation and animal welfare concerns. However, the higher level of concern expressed by female, low engagement and welfare-oriented participants for activities that involve direct killing indicates a need for wildlife managers to consult beyond traditional stakeholders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".