Human behaviour and perceptions of outdoor pet cats in an urban environment
Bibliographic record
Abstract
The risks that face both wildlife and cats while cats are outside unsupervised is high and momentum surrounding the global issue of outdoor domestic cats (Felis catus) and their impact on wildlife, especially birds, is gaining the attention of experts working in the fields of wildlife conservation and animal welfare. The purpose of this research is to gain an understanding of cat owner behaviour, and the views of non-cat owners and their role, with respect to outdoor pet cats, in the community of Kamloops, BC. The thesis chapters include views and opinions about wildlife and outdoor pet cats and their owners in general, as well as an analysis of Kamloops residents’ perceptions of the risks outdoor pet cats impose on the environment and incur while outdoors. It explores economic theories, such as, the value of a statistical life of a cat, negative externalities and Coase Theorem. An online survey was used for data collection; 584 valid survey responses were received and respondents were comprised of 155 outdoor cat owners, 221 indoor cat owners and 208 non-cat owners. Key findings include: the outdoor cat owners, despite their perceived risks, see outdoor pet cats living happier lives, and that they play a useful role as predators; the average value of a statistical life of a cat in Kamloops is $7,485 to $8,726; nearly 40 percent of the outdoor cat owners are willing to reduce the number of hours they allow their cat outdoors unrestricted/unsupervised to keep their cat safe; and 37 percent of non-cat owners are willing to pay to keep their neighbours cat in the cat owner’s yard. Coase Theorem application suggests the solution that would create the least conflict in the community, yet still allow the cat owners to have supervised outdoor cats, is with the application of a bylaw coupled with licensing. In general, though, cat owners do not see how licensing would be useful. The best immediate use of limited resources that will be impact-driven and community-specific, will be a campaign targeting potential cat owners and new cat owners, while working towards a longer term solution that includes mandatory licensing, a bylaw, and delivery of practical information for cat owners on how to keep their cat under their care and control while outdoors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".