Gender and pathways to housing: in search of affordable and safe rental housing in a mid-sized Canadian city (Kelowna, B.C.)
Bibliographic record
Abstract
Housing is not merely a commodity but a fundamental human right and crucial for a good quality of life. In Kelowna, B.C., a city known for its high housing costs, women encounter significant obstacles in securing affordable rental housing. This study involving 25 women renting in Kelowna sheds light on their preferences, concerns, and strategies for navigating this challenging housing market. The research highlights that women prioritize rental units that are not only affordable but also safe and well-located. Key barriers identified include the high cost of housing, scarcity of affordable rental options in desirable neighborhoods, inadequate safety features in rental units, and discrimination based on income sources. Data was gathered through self-administered questionnaires and semi-structured interviews with ten key informants, revealing pervasive challenges and suggesting policy recommendations. These insights contribute to understanding the specific hurdles women face in securing affordable, suitable and adequate rental housing in mid-sized Canadian cities like Kelowna. Addressing these barriers requires multifaceted solutions, including increasing affordable housing options, improving safety standards, and policies that combat income-based discrimination. By implementing these measures, Kelowna can enhance housing accessibility and quality of life for all residents, particularly women struggling in the rental market.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".