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Record W3170396594 · doi:10.1093/police/paab030

Exploring the Roles and Function of Police Search and Rescue Teams in Canadian Agencies

2021· article· en· W3170396594 on OpenAlexaffabout
Lorna Ferguson, Janne E. Gaub, Laura Huey

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsFunction (biology)Thematic analysisHierarchyPublic relationsDutyWork (physics)Political sciencePsychologyQualitative researchEngineeringSociologyLaw

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.229
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.183
GPT teacher head0.462
Teacher spread0.279 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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Same venuePolicing A Journal of Policy and PracticeSame topicHomelessness and Social IssuesFrench-language works237,207