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Enregistrement W4311954874 · doi:10.1093/acrefore/9780190264079.013.737

Rural and Remote Policing

2022· reference-entry· en· W4311954874 sur OpenAlexaff
Rick Ruddell

Notice bibliographique

RevueOxford Research Encyclopedia of Criminology and Criminal Justice · 2022
Typereference-entry
Langueen
DomaineSocial Sciences
ThématiqueCrime Patterns and Interventions
Établissements canadiensUniversity of Regina
Organismes subventionnairesnon disponible
Mots-clésScholarshipRural areaIndigenousMetropolitan areaCriminologyGeographyPoliticsPolitical scienceEconomic growthSocioeconomicsSociologyLaw

Résumé

récupéré en direct d'OpenAlex

Abstract Although many city residents think of life in the countryside as peaceful, there are rural and remote communities with very high crime rates. In addition, some rural populations, such as Indigenous peoples and women, are at higher risk of victimization than their urban counterparts. Regardless of where one lives, levels of crime in the countryside and the formal and informal responses to those acts—including policing—have been shaped by a series of historical, political, geographic, economic, and demographic factors, and many of those factors are interconnected. Rural crime is further distinctive as some offenses, including illegal hunting (poaching) or environmental crimes, rarely occur in cities. Moreover, responses to those acts may be carried out by military organizations, nonpolice authorities, and police officers. The involvement of these quasi-police organizations in responding to rural crime is increasing in some nations. Rural is defined in this entry as a community or place with fewer than 2,500 residents located at least 30 miles from the nearest metropolitan area. Despite recognizing that crime in the countryside is unlike what occurs in cities, there has been comparatively little scholarship on rural or remote policing. Instead, most police research is conducted and disseminated by urban researchers—what some call an urban-centric focus—and as a result knowledge about rural policing is underdeveloped. There has been even less scholarship focusing on policing remote communities, and that is a significant limitation given the distinctive patterns of crime in some of these places. Although policymakers have developed a diverse range of interventions to respond to antisocial behavior, disorder, and crime in rural and remote jurisdictions, the people in these places have a common expectation: They want the same quality of policing city residents receive. The nature of rural policing, however, makes that a very difficult goal to achieve, as officers are often stretched thin and work in more dangerous conditions than their urban counterparts. Rural officers are also expected to respond to every conceivable call for service even though they often work alone and have limited backup. This is because many stand-alone rural police services are cash strapped as they draw from sparsely populated or impoverished tax bases. Inadequate funding also limits their ability to recruit officer candidates and inhibits the sophistication of investigations, opportunities for officer training, officer retention, and the ability to provide safe working conditions for their personnel. Four issues are of key importance to understanding rural and remote policing: (a) Rural crime differs from urban crime, and in some jurisdictions, the volume of crime is similar to (or greater than) city crime rates, although the nature of crime differs (e.g., some types of crimes occur more often in rural places); (b) rural officers carry out their day-to-day duties in a distinctively different manner than municipal officers; (c) the informal and formal expectations for rural officers are higher than their counterparts working in urban areas; and (d) the challenges of rural policing are magnified in remote locations.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,874
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0020,002
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,148
Tête enseignante GPT0,432
Écart entre enseignants0,284 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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