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Rural and Remote Policing

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

Bibliographic record

VenueOxford Research Encyclopedia of Criminology and Criminal Justice · 2022
Typereference-entry
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsScholarshipRural areaIndigenousMetropolitan areaCriminologyGeographyPoliticsPolitical scienceEconomic growthSocioeconomicsSociologyLaw

Abstract

fetched live from 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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.148
GPT teacher head0.432
Teacher spread0.284 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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