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Record W4281749368 · doi:10.1080/15377938.2022.2081643

Learning from indigenous youth to build relations and earn trust in policing

2022· article· en· W4281749368 on OpenAlexaff
Leisa Desmoulins, Melissa Oskineegish, Kelsey Jaggard

Bibliographic record

VenueJournal of Ethnicity in Criminal Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsLakehead University
Fundersnot available
KeywordsIndigenousMainstreamCommunity policingDialogical selfFocus groupAccountabilityPerceptionSociologyPublic relationsPsychologySocial psychologyCriminologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

As part of a local police service’s larger organizational change initiative, this study explored trust in policing. A cultural safety lens was used to hear Indigenous youths’ truths and experiences. Methods comprised three focus groups with 19 participants (age 18-30 years) in the spring of 2019. Researchers employed a three-phase procedure to code and analyze the data. Findings highlight factors that led to participants’ mistrust and perceptions of biased policing. Participants also recommended four ways for police to gain their trust through education, community engagement, respectful relations, and accountability. Implications for practice ensure a culturally safe approach for police and other mainstream organizations to follow for institutional changes that promote trust and reconciliation. Conclusions and next steps follow for an iterative, dialogical process for cultural safety.

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)0.010
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0130.006
Scholarly communication0.0050.004
Open science0.0010.009
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.104
GPT teacher head0.395
Teacher spread0.290 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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