Aspects of Police Search and Rescue Work for Missing Persons in Canada
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,032 | 0,006 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».