Integrating animal welfare into wildlife policy: a comparative analysis of coyote management programs in California, United States and Ontario, Canada
Bibliographic record
Abstract
Coyotes (Canis latrans) are native to North America and are frequently seen in and around urbanized areas. As human population grows and urban sprawl encroaches on coyote habitat, human-coyote conflicts increase. Faced with the need to find solutions, policy-makers, and conservationists are challenged with the task of designing coyote management programs that would ensure public safety while conserving the species. The need to consider the welfare of individual animals, as encompassed by the emerging field of Compassionate Conservation, adds an additional challenge. By examining two coyote management programs’ case studies in North America—one in Long Beach, California and another in Oakville, Ontario—the benefits of adopting compassionate solutions are illustrated. As exemplified by Oakville’s strategy, compassionate programs promote the moral treatment of animals while proving to be economically and socially superior to strategies employing lethal measures. Such strategies adopt proactive, rather than reactive responses to human-coyote encounters and invest heavily in public engagement and education. Through the development, implementation, and regulation of non-lethal wildlife management policies, more cities and towns will be able to meet the needs of the stakeholders involved in coyote-human conflict while sparing the life of the animal.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".