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Record W3113398110 · doi:10.18357/ijcyfs114.2202019988

POLICE STUDIES PROGRAM FOR YOUTH AT RISK: THE ROLE OF POLICE DISTRIBUTIVE JUSTICE AND PERSONAL MORALITY IN EXPLAINING POLICE LEGITIMACY

2020· article· en· W3113398110 on OpenAlexvenueno aff
Ameen Azmy

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsMoralityLegitimacyProcedural justiceDistributive justiceSocial psychologyIntervention (counseling)PsychologyPerceptionEconomic JusticeCriminal justiceCriminologyPolitical scienceLawPsychiatry

Abstract

fetched live from OpenAlex

This study examined a unique police studies intervention program by comparing two groups of youth-at-risk in two types of residential youth schools. The experimental group included 129 youths who had attended a police studies program, while the control group included 167 youths who had attended a different intervention program, without police studies. We hypothesized that the experimental group would have more positive perceptions of police legitimacy and distributive justice and higher levels of personal morality than the control group would. Moreover, we hypothesized that the relationship between the type of the intervention program and perceptions of police legitimacy would be explained by youths’ personal morality and perceptions of police distributive justice. The study showed that the experimental group had more positive perceptions of police legitimacy and higher personal morality than did the control group, but there were no differences in perceived police distributive justice between the two groups. In addition, while personal morality partly mediated the link between the type of intervention program and perceptions of police legitimacy, perceived police distributive justice did not. Empirical and theoretical implications 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.111
GPT teacher head0.414
Teacher spread0.303 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2020
Admission routes1
Has abstractyes

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