Why can't we be friends? Exploring factors associated with cat owners' perceptions of the cat-cat relationship in two-cat households
Bibliographic record
Abstract
Most research examining cat behavior in multi-cat households lacks focus on one group size. This gap in knowledge reduces generalizability of research findings to specific compositions of cats in multi-cat households. Given that many cat-owning households in Canada and the US are comprised of two cats, the following study used a cross-sectional survey to explore cat owners' perceptions of the cat-cat relationship in two-cat households in Canada and the US. A total of 6,529 owners of two cats completed the online questionnaire. Descriptive statistics were used to summarize the data and a logistic regression model used to assess various explanatory variables (i.e., household, management, and cat-specific factors) associated with participants perceiving their cats' relationship as negative. The logistic regression model showed that owners of two-cat households are more likely to perceive their cats' relationship as negative if both cats are spayed females, adult or mature, have a large gap in age, not related, one or both have access to the outdoors, or show aggression toward people or other animals in the home. Having multiple litterbox and feeding areas were also associated with a more negative cat-cat relationship. Overall, the complex interplay, directionality, and temporality of these factors requires further investigation for a full understanding of how to improve the cat-cat relationship in two-cat households. More research is needed to provide evidence-based recommendations for managing and supporting a positive cat-cat relationship in the home.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".