Bibliographic record
Abstract
While there is a plethora of research focusing on the lives of women who experience long-term homelessness, there are very few examples of studies that meaningfully engage women to imagine the type of housing and support that they believe they require to resolve their homelessness. The Women Envisioning Support and Housing (WESH) project responds to this gap in knowledge, by engaging twenty-one women experiencing long-term homelessness, without children in their care, living in Hamilton, Ontario, in a creative process of imagining the type of housing and support they desire. Guided by the principles of feminist participatory action research (FPAR) and informed by an intersectional feminist analysis, the WESH project centres the experience, knowledge and intersecting identities of women who experience long-term homelessness in a process of co-creating knowledge about gender-specific housing and support. Research was conducted during the COVID-19 pandemic and used virtual creative mapping workshops as a method for data collection. Workshops were conducted individually with participants over Zoom and drew on a series of arts-based and narrative activities to explore key elements of housing and support including geographical location (place), spatial design (including infrastructure, individual space and communal space), policies and practices, on-site supports and elements of community and social connection. Findings highlight that despite differing visions for housing and support, participants largely agreed on a set of shared values to inform gender-specific housing including safety, participation, care and community. This research contributes to feminist housing scholarship, activism, policy and practice, by grounding an exploration of gender-specific housing and support programs in women’s lived experiences and preferences. In doing so, the WESH project offers a value-based framework for the creation of more participatory, caring, and sustainable housing solutions by/for women with long-histories of homelessness.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.166 | 0.034 |
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".