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Record W3016769992 · doi:10.1177/1057567720918640

“Did Not Return in Time for Curfew”: A Descriptive Analysis of Homeless Missing Persons Cases

2020· article· en· W3016769992 on OpenAlexaffabout
Laura Huey, Lorna Ferguson

Bibliographic record

VenueInternational Criminal Justice Review · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsCurfewMissing dataDescriptive statisticsPopulationCriminologyScholarshipPsychologyPolitical scienceMedicineSociologyDemographyLaw

Abstract

fetched live from OpenAlex

Homeless communities have garnered recent public attention in Canada due to their high rates of violence, victimization, and being reported as missing. There have been several high-profile cases, investigations, and inquiries involving missing homeless persons, yet very little is known about what cases are reported to the police, under what circumstances they go missing, and the outcomes of those cases. As a result, the purpose of this study is to provide some insights into some of these unresolved issues by offering an exploratory, descriptive analysis of 291 closed missing person cases from the records of a municipal police service. What this analysis reveal is a somewhat more mundane picture. Specifically, results indicate that the majority of missing person reports are of those who are female and White, have a drug/alcohol addiction, are residing at homeless shelters/missions, and have a history of being reported as missing. As well, it was revealed that most people are reported as missing due to shelter/mission reporting issues with curfews and that all are located alive. This study extends the minimal existing scholarship on the missing homeless population by providing some preliminary insights on the vulnerabilities and factors that can impact these cases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.204
GPT teacher head0.474
Teacher spread0.270 · 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 teacher head, 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

Citations15
Published2020
Admission routes2
Has abstractyes

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