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Record W4396865833 · doi:10.1177/00938548241249610

Women’s Homelessness and the Justice System: A Study of Desistance and Social (Re)integration Among Canadian Women Who Used or Did Not Use Criminal Activities to Survive

2024· article· en· W4396865833 on OpenAlexaffabout
Mathilde Moffet-Bourassa, Isabelle F.-Dufour

Bibliographic record

VenueCriminal Justice and Behavior · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsProsocial behaviorCriminal justicePsychological resilienceEconomic JusticeCriminologyPsychologyHuman factors and ergonomicsPoison controlSuicide preventionSocial psychologyPolitical scienceMedicineMedical emergencyLaw

Abstract

fetched live from OpenAlex

Women experiencing homelessness (WEH) are a marginalized group who often lack support services, leading them to adopt alternative survival strategies that leave them at risk of being victimized and becoming involved with the justice-system. To better understand this problem, we analyzed the adaptive strategies Canadian WEH use to survive. Comparing the life histories of WEH who turned to criminal activities ( n = 4) with WEH who demonstrated prosocial resilience ( n = 4) makes it possible to identify protective and risk factors for criminal behavior and to propose adaptive strategies that can be used to support these women to adopt a prosocial lifestyle. The results provide a framework for understanding the needs of WEH, filling a gap that results from the focus on men needs in most scientific literature and by many resources, and suggesting that meeting those needs may reduce the likelihood that WEH will become involved with the justice-system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0160.004
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.112
GPT teacher head0.390
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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