“Every Time I Try to Get Out, I Get Pushed Back”: The Role of Violent Victimization in Women’s Experience of Multiple Episodes of Homelessness
Bibliographic record
Abstract
Research shows that, for most people, homelessness is not a chronic state that one enters and never leaves. Instead, homelessness tends to be dynamic, with individuals cycling in and out of multiple periods of homelessness throughout their lives. Despite this recognition, and a wealth of research on the causes of homelessness, generally, there is a lack of scholarship on the pathways to multiple episodes of homelessness. In particular, the relationship between violent victimization and women’s likelihood of being homeless multiple times is largely unexplored. Drawing on data collected from 269 structured interviews conducted with women using the services of homeless shelters and/or transitional housing in three U.S. and two U.K. cities, we use multivariate logistic regression to assess whether violent victimization increases women’s likelihood of experiencing multiple episodes of homelessness. Our results show that adult victims of stranger-perpetrated physical assault are significantly more likely to be homeless on multiple occasions. In addition, those who experience multiple forms of victimization (e.g., physical and sexual abuse) in childhood, adulthood, and/or across the life course are significantly more likely to experience multiple episodes of homelessness. Given recent efforts to eradicate homelessness, our results suggest specific vulnerable groups that may benefit from targeted social and policy interventions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".