“Giving the Highest Chance of a Good Outcome”: Exploring the <i>Missing Persons Act</i> in British Columbia and Ontario from the Policing Perspective
Bibliographic record
Abstract
British Columbia and Ontario are two of the Canadian provinces and territories that have enacted a Missing Persons Act, legislation aimed at improving the police investigation of missing person cases. Understanding the Acts in these regions from the policing perspective presents an opportunity to assess their efficacy and utility. Therefore, the purposes of this study are to examine police perceptions of and experiences with the Missing Persons Act in each region. Through in-depth, semi-structured interviews with police officers from over 20 services across these regions, this article explores police insights into the impacts, challenges, and benefits of the Acts related to missing persons work. Additionally, police support for and perceptions of this legislation are uncovered. Results show that police view the Acts in these regions as enhancing missing persons work through standardization and strengthening abilities to obtain information and records, follow various leads, and use technologies that assist in successfully locating missing people. However, a paradox emerged: Police are reluctant to make use of this legislation. Explanations for this and the implications of these findings are discussed.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".