Solutions-Based Approach to Urban Cat Management—Case Studies of a One Welfare Approach to Urban Cat Management
Bibliographic record
Abstract
Urban free-roaming cats create concern about their impacts on wildlife and human health, leading to the use of trap-adopt-kill methods to manage these populations. This method is ineffective at decreasing the free-roaming cat population and has a negative impact on cat caregivers' well-being. Using semi-structured interviews, this study explored the relationship that semi-owners (people who feed cats but do not perceive ownership) and owners of multiple cats have with the cats they care for, and the social and psychological impacts of an alternative assistive-centered approach to urban cat management. This approach to semi-owned and owned cats provided free sterilization and preventative healthcare. Our findings demonstrate that the caregivers had a strong emotional bond with the cats they cared for. The caregivers also experienced a positive impact on their quality of life, and indicated an improvement in the cats' welfare after having the cats sterilized through this program. Additionally, the cat caregivers indicated that they had a negative view of agencies, such as the municipal council. It is recommended that an assistive-centered approach to urban cat management be prioritized by local councils and welfare agencies to improve caregivers' quality of life and psychological well-being, whilst also improving cat welfare. The implementation of this assistive-centered management approach could improve the relationship between communities and the agencies involved, leading to the continuous reporting of free-roaming cats for sterilization. This assistive-centered approach has the potential to reduce the free-roaming cat population, their effects on wildlife, nuisance complaints, and council impoundments, and is aligned with the One Welfare philosophy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".