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Record W4221000371 · doi:10.35502/jcswb.222

Offenders on judicial orders: Implications for evidence-based risk management in policing

2022· article· en· W4221000371 on OpenAlexaffvenueabout
Sandy Jung, Gregory Kitura

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsMacEwan University
Fundersnot available
KeywordsNeglectLaw enforcementMental healthPsychologyRehabilitationEnforcementCriminologyService (business)Risk managementPsychiatryPolitical scienceLawBusiness

Abstract

fetched live from OpenAlex

There is little known about individuals who serve judicial protective orders called Section 810.1 and 810.2 peace bonds. Many Canadian police services provide supervision of these individuals, who are deemed high risk for violence, yet little research has been done on community supervision by police. The current study profiles the characteristics of 45 adult supervisees who were serving 810.1 and 810.2 orders and supervised by a local police service. The findings indicate that a majority of these individuals have experienced childhood abuse and neglect, lack high school education, were exposed to parental alcoholism, and demonstrated evidence of mental health problems. Further, and perhaps less surprising, they had remarkable histories for criminal behaviour, in terms of frequency, severity, and antisocial behaviour. Most of the individuals had criminogenic risk factors and responsivity issues that required attention at the start of their supervision. This study highlights the high needs of individuals under judicial orders and provides insight into the level of resources needed to supervise them. Implications for training law enforcement in applying effective principles of rehabilitation and risk assessment are 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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
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 head0.082
GPT teacher head0.386
Teacher spread0.304 · 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 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

Citations5
Published2022
Admission routes3
Has abstractyes

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