Integrating animal welfare into wildlife policy: a comparative analysis of coyote management programs in California, United States and Ontario, Canada
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
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.
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 | 0.000 | 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