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Record W68999434

Incidents of assault in the night time economy: the influence of supervision, training and culture upon officer decision

2014· dissertation· en· W68999434 on OpenAlexaboutno aff
James Fleming

Bibliographic record

VenueUPT. Syiah Kuala University Library (Syiah Kuala University) · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsOfficerDiscretionMisconductFront linePublic relationsPsychologyScrutinySeriousnessLaw enforcementApplied psychologySocial psychologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

This dissertation investigates the underpinnings of the arrest-decisions by front-line police officers at incidents of assault in the Night Time Economy. A qualitative approach was utilized, drawing primary data from field-work at three eastern Canadian services; an extensive search of previous literature provided relevant secondary data. The study sought to provide some understanding of why such arguably common occurrences may be handled by officers in a wide variety of ways – the results add another perspective to the literature relevant to NTE violence, and may be of use by administrators, supervisors and trainers towards improvements in policing practice and public safety. Specific scrutiny was given to the influences of training, supervision and culture. The study found that the majority of officers can recall little, if any, formal training in the theory, application or implications of discretion; most importantly, including the theorization that front-line police decisions define street-level policy. Further, due to a dearth of organizational and supervisory direction, officers learn how they might administer a bar-assault from others in their work-group; illustrated by the predominance of personal and shared perceptions towards the victim including a subjective determination of the seriousness of the assault. The implications of officer work-load, along with suggestions of avoidance and misconduct were also identified. The research provides recommendations toward improving practice through training and organizational direction specific to bar-assault, which would include input from all actors participating in the NTE.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0020.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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