Finally home: Housing that works for women who have experienced homelessness
Bibliographic record
Abstract
This research explores the question: What makes housing work for women who have experienced homelessness on Vancouver’s Downtown Eastside (DTES)? Eleven women were interviewed, both in-depth interviews and tours of the women’s sleeping places. During the interviews and tours, trends and priorities were identified in terms of the housing type, choice, housing with or without a partner, design of the space, accessibility, safety, guidelines and policies, repairs and cleanliness, support from staff and programming. Interviews were also completed with experts in housing or homelessness to supplement the information heard from women. Experts included people involved in planning, finding, providing, or researching housing. Information from experts expanded on, confirmed and provided context to the findings from the women’s interviews. Engaging with women allowed them to provide this project with their experience and recommendations in the planning, design, management and provision of housing. Through this research, functional solutions were uncovered to provide better housing that works for women. The information gathered is useful to inform policy, planning, funding, design, and support services in order to better provide women with more than a roof over their heads, and to help them find a place to finally call 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".