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Record W2165152267 · doi:10.1080/10439461003668476

Conceptual framework for managing knowledge of police deviance

2010· article· en· W2165152267 on OpenAlexaboutno aff
Geoff Dean, Peter Bell, Mark Lauchs

Bibliographic record

VenuePolicing & Society · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
FundersSingapore Police Force
KeywordsDeviance (statistics)MisconductConceptual frameworkPositive devianceSociologyCriminologyPsychologyPolitical scienceSocial psychologyComputer scienceLawSocial science

Abstract

fetched live from OpenAlex

This is a conceptual article on police deviance and its multi-faceted forms. It seeks to address the lack of an adequately formulated framework in the literature of the breadth and depth of police misconduct and corruption. The article argues for the use of a proposed ‘sliding scale’ of police deviance by examining the nature, extent and progression of police deviance and crime using research in Australia and Canada as illustrative case studies. This sliding scale is designed to research, capture and store, and hence extend the knowledge base of what constitutes police deviance at the level of the individual, the group and the organisational contexts of policing. As such, the conceptual framework is a robust yet flexible research tool and its utility as a sliding scale constitutes a step forward in advancing the knowledge on police deviance and criminality through adopting an integrated and holistic approach to managing such knowledge.

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)0.015
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0120.008
Science and technology studies0.0050.024
Scholarly communication0.0100.020
Open science0.0050.009
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.063
GPT teacher head0.416
Teacher spread0.353 · 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 designTheoretical or conceptual
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

Citations35
Published2010
Admission routes1
Has abstractyes

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