No Pets Allowed: Landlord Attitudes Towards Pets in a Small Canadian City
Bibliographic record
Abstract
Abstract Pet ownership is associated with greater mental and physical well-being, but it can also make some aspects of life more difficult. For example, many landlords have a ‘no-pets’ policy, which is especially problematic in areas where rental units are already in short supply. We examined landlord attitudes towards tenants with pets in a small Canadian city with a consistently low vacancy rate. Sources of data included classified advertisements over a 15-year period and telephone interviews with 32 landlords currently advertising rental units. Questions examined included: has the proportion of landlords adopting a no-pets policy increased over time; which specific concerns do landlords have about pets; and what factors, if any, might increase landlords’ willingness to allow pets? Our analyses indicated an increase over time in the proportion of ads explicitly stating a no-pets policy, from less than a third in the early 2000s to more than half in the most recent years examined. These proportions were related to fluctuating vacancy rates. Interviews with landlords revealed concerns about pets that focused mostly on potential damage to rental units, with their concerns sometimes based upon past experiences. However, landlords also reported encountering similar problems when renting to tenants without pets, and of the 18 landlords who had initially posted ads explicitly stating a no-pets policy, 8 said that they would nevertheless consider pets under some conditions. Pet owners may be able to increase their ease of finding housing by addressing these factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".