Exploring the Roles and Function of Police Search and Rescue Teams in Canadian Agencies
Bibliographic record
Abstract
Abstract Police search and rescue (SAR) teams play a vital part in the successful location of lost and missing persons; however, they remain an understudied policing component. Therefore, the purpose of this study is to improve and deepen scholarly knowledge of the police SAR aspects of missing persons cases. Specifically, this article aims to provide first insights into the roles and function of SAR teams in Canadian police services. With this research, we can begin to formulate a better understanding of their utility in police missing persons work. Through a thematic analysis of 34 in-depth, qualitative interviews with police SAR team members from 13 police services across Canada, we explore how SAR teams operate within police services and outline the varying roles police personnel comprise in these teams. Results reveal that police SAR teams operate with several distinct roles that have different functions within the larger police hierarchy. Furthermore, findings show that police SAR personnel are fulfilling a host of responsibilities in these teams while operating as a secondary duty, yet are called upon at any time and are required to respond immediately. These findings and their implications for police missing persons work are then 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".