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Record W4225368710 · doi:10.1080/01924036.2022.2071308

Perceptions of trust in the police: a cross-national comparison

2022· article· en· W4225368710 on OpenAlexaff
Rick Ruddell, Kelsey Trott

Bibliographic record

VenueInternational Journal of Comparative and Applied Criminal Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsVictimisationPerceptionPer capitaPublic trustGross domestic productTrustworthinessCriminologyCriminal justiceDemocracySocial psychologyFear of crimePsychologySocial trustPolitical scienceHuman factors and ergonomicsSociologyPublic relationsPoison controlLawSocial capitalEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Comparative analyses enable researchers to identify individual- and structural-level factors that influence the operations of the justice system that might not be evident when examining these indicators in a single nation. In this study, the factors associated with the public’s self-reported trust in thepolice were examined in 105 nations. We analysed the contributions of three theoretical propositions: social integration, democratic performance, and self-reported perceptions of crime. With respect to the structural factors, the public expressed the most trust in the police in nations with a greater adherence to the rule of law and a higher per capita gross domestic product. Citizens in countries perceived to be more corrupt were also less likely to believe their police were trustworthy. Inconsistent with expectations, individual-level factors, such as perceptions about crime, risks of being victimised, and prior histories of victimisation also exerted an influence on the public’s perceptions of trust. .

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)0.003
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.136
GPT teacher head0.489
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 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

Citations16
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Comparative and Applied Criminal JusticeSame topicPolicing Practices and PerceptionsFrench-language works237,207