Aspects of Police Search and Rescue Work for Missing Persons in Canada
Bibliographic record
Abstract
A three-year-old toddler stuck for eight days in a hidden ditch on a 60-acre farm. A 92-year-old woman with dementia lost on a section of well-used train tracks. A teenager experiencing suicidal thoughts while hiding atop a mountain. These are real missing persons cases, revealed through my extensive research conducted with police in Canada who perform one of the most critical and least understood tasks within policing: search and rescue (SAR).\nWhile many missing persons cases reported to the police in Canada are successfully resolved within 48 hours with the individual located safe and well, some are significantly more challenging because of unique aspects of the individual’s situation, including their physical or mental capacity, the location from which they went missing, the terrain in which they are most likely to be found, or environmental conditions that hamper their discovery or pose a threat to their physical safety. These complex cases of missing individuals fall to police SAR personnel to find and/or rescue. As a part of Canada’s complex SAR system, police play a significant part in the successful resolution of missing persons reports.\nDespite this, there is a shortage of literature on this area of police work, resulting in several public and scholarly calls for research dedicated to uncovering what the police do, how effective they are at doing it, and what can be improved in SAR. This dissertation attempts to fill such gaps in understanding by analyzing aspects of police SAR work. It does so by undertaking a sociological analysis of police SAR personnel’s work individually and collectively and with respect to the organization of policing. This research also analyzes what works, what does not, and what can be done better to generate scientific insights that can be used for bettering police practice and policy and advancing the knowledge base as part of the calls in the global evidence-based policing movement.\nTo do so, it draws from a collection of data, including over 200 in-depth interviews and surveys with police and thousands of different types of police missing persons records. Laced with the stories of missing persons, it presents a detailed overview of what these personnel do, the processes and procedures employed in this work, and the tools and technologies in SAR. It further explores some of the strengths of this work and the challenges impacting police SAR responses. This dissertation also identifies future trends to address the “what may be next” question in the police SAR response to missing persons. Ultimately, the insights gleaned from this dissertation not only offer understandings of this area of policing but also provide practical recommendations for improving police SAR work, which serves the broader goal of safeguarding communities and saving lives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".