Enhancing Access to Quality Rental Housing for People with Pets as Healthy Public Policy
Bibliographic record
Abstract
When pets are considered in housing studies, attention tends to be paid towards vulnerable pet owners, namely in the context of homelessness, domestic violence situations, or disaster circumstances. Targeted interventions are important since vulnerable pet owners may risk their lives if they anticipate being separated from their pets or being turned away from shelters because of them. Intervention strategies that target the whole population are also needed, however, since pets are regularly restricted, if not banned outright, from the private rental sector and in social housing. Moreover, a “no pets” policy may force homeowners, specifically condominium or strata owners, to give up their pets. This thesis is comprised of three papers linking housing, health, and pet ownership in Calgary, Alberta, Canada. As a relatively new area of study, issues surrounding housing accessibility, affordability, location, and quality among pet owners were mainly explored qualitatively. The first paper drew upon online rental listings and focused on housing recovery for tenants with pets in the aftermath of a flood. The second paper moved beyond disaster circumstances and compared perspectives towards pets in rental housing more generally. Finally, the need to address housing issues for pet owners must be considered within the context of social, economic, and demographic pressures. Millennials not only represent a majority of pet owners today, they are also disproportionately tenants and they tend to move frequently. As a result, the third paper considered what life is like for millennials with dogs once they are housed in the rental market, paying close attention to the potential influence of pet ownership on their identities, relationships, and environments. Overall, this thesis begins to answer important questions about how restrictive policies on pets in rental housing impacts human health and development. Without the proper supports in place, people with access to fewer resources may face greater challenges keeping their pets, because they cannot opt for homeownership. Improvements in access to housing for pet owners, and integrated programs and services to support pet ownership in housing and neighbourhood contexts, are essential to addressing animal relinquishment and to reducing inequities in health outcomes among pet owners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".