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Record W2464436105 · doi:10.1177/0004865816656721

Public assessments of the police and policing in Hong Kong

2016· article· en· W2464436105 on OpenAlexaff
Michael Adorjan, Maggy Lee

Bibliographic record

VenueAustralian & New Zealand Journal of Criminology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLegitimacyPolice scienceCriminologyContext (archaeology)SociologyPoliticsPublic orderPublic relationsCrime preventionPolitical scienceCriminal justiceLaw

Abstract

fetched live from OpenAlex

This paper presents the findings from a focus group research study on public assessments of the police and policing in Hong Kong. The main findings indicate that while people have generally positive views about police effectiveness in responding promptly to and fighting crime, they have decidedly mixed views regarding stop and search and public order policing. By drawing on the multi-dimensional framework of trust proposed by other policing scholars, we suggest that a useful way to conceptualize public assessments of the police and questions of satisfaction and trust of policing in Hong Kong is to distinguish between people's instrumental concerns about personal safety and crime and their affective concerns about the process of policing and the symbolic role of the police in maintaining a particular way of life. The paper concludes by reaffirming the value of sociologically informed, qualitative policing research that examines questions of police-citizen relationship and legitimacy within a broader socio-political context.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.236
GPT teacher head0.417
Teacher spread0.181 · 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 designObservational
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

Citations12
Published2016
Admission routes1
Has abstractyes

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