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Record W2972780028 · doi:10.1093/police/paz053

Community Policing in Schools: Relationship-Building and the Responsibilities of School Resource Officers

2019· article· en· W2972780028 on OpenAlexaffabout
Ryan Broll, Stephanie Howells

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsContext (archaeology)Element (criminal law)Law enforcementResource (disambiguation)Triad (sociology)Public relationsPolitical scienceSociologyLawComputer scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

Abstract School resource officers (SROs) have become nearly ubiquitous in North American schools in the last three decades. Most research on SROs has examined their impact on violence and disorder at school, yielding mixed results; however, it is widely accepted that traditional law enforcement responsibilities comprise only one element of SROs’ triad of responsibilities, which also includes teaching and counselling. Although their responsibilities are based in community policing models, little research has explored the place of community policing principles within the work of SROs. Drawing upon mixed methods data collected from school administrators and SROs in a large Canadian city, this study examines relationship-building within the context of SROs’ triad of responsibilities. The results suggest that SROs follow a community policing approach and strategically foster mutually beneficial relationships to support their law enforcement, teaching, and counselling objectives. Further, as a result of their established relationships, SROs are positioned as key sources of support for school administrators.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.007
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.090
GPT teacher head0.455
Teacher spread0.366 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePolicing A Journal of Policy and PracticeSame topicEducation Discipline and InequalityFrench-language works237,207