“Did Not Return in Time for Curfew”: A Descriptive Analysis of Homeless Missing Persons Cases
Bibliographic record
Abstract
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 reveals 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.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".