“Pets Negotiable”: How Do the Perspectives of Landlords and Property Managers Compare with Those of Younger Tenants with Dogs?
Bibliographic record
Abstract
Previous research has shown that housing insecurity contributes to animal relinquishment and that tenants with dogs face disadvantages in the rental market. Still, little is known about how dog owners navigate rental markets, nor how landlords and property managers perceive dogs and other pets. This case study reports on in-depth interviews with younger tenants with dogs and on open-ended survey responses from landlords and property managers. In their housing searches, tenants with dogs reported feeling powerless in negotiations and feeling discriminated against. They described settling for substandard properties, often located in less desirable neighborhoods. Also, some said they felt obliged to stay put in these rentals, given how difficult it had been to find a place that would accommodate their dogs. Meanwhile, landlords and property managers indicated that listings advertised as "pet-friendly" tend to receive more applicants than listings in which pets are prohibited. Suggestions for improvement included meeting pets prior to signing the lease; getting everything in writing; steering clear from furnished units; charging utilities to tenants; and speeding up the pet approval process when dealing with condominium boards. These suggestions offer implications for future research, partnerships, and policy options to improve the prospects of pets and their people in rental housing.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".