A gap in knowledge surrounding urban housing interventions: a call for gender redistribution
Bibliographic record
Abstract
Using gender-sensitive (ensures that resource distribution considers gender) and gender-redistributive (aims to develop balanced gendered relationships through redistributing resources) analytic lenses in urban health interventions is long overdue. The social construction of gender and its impact on the health of marginalized women, especially women who experience homelessness within urban settings is frequently overlooked. Housing research, programs, and policies too often fail to utilize gender redistributive frameworks-perpetuating gendered harm for many. This article provides an update of current practices around consideration of gender in housing interventions and literature while advocating for the necessary incorporation of gender-redistributive practices in housing research and program implementation. Addressing these gaps will address the longstanding neglect that has led to disparities among women who experience homelessness or housing insecurity.
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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 | 0.062 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 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".