MétaCan
Menu
Back to cohort
Record W3201165273 · doi:10.35502/jcswb.201

Why Indigenous Canadians on reserves are reluctant to complain about the police

2021· article· en· W3201165273 on OpenAlexaffvenueabout
John Kiedrowski, Michael Petrunik, Mark Irving

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIndigenousCriminologyMisconductAgency (philosophy)Criminal justiceEconomic JusticePolitical sciencePublic relationsSociologyLawSocial science

Abstract

fetched live from OpenAlex

Recent widespread protests and intensive media coverage of actual and alleged acts of police misconduct against members of vulnerable populations (e.g., Indigenous and racialized persons, mentally ill and/or addicted persons) overrepresented in the criminal justice system have renewed interest internationally in the factors influencing civilian complaints against police. In Canada, a major concern exists regarding how Indigenous persons who feel improperly treated by the police perceive and confront barriers to making formal complaints about such treatment. This study focuses on the Royal Canadian Mounted Police (RCMP), the police agency providing services to the majority of rural and northern reserve communities. Our survey and interviews with influential “community informants” (in this instance community court workers) with intimate knowledge of such local communities, shared culture and language, and vicarious appreciation of the experiences of community members support the view that Indigenous persons do encounter significant barriers to launching formal complaints and are consistent with other research literature. We discuss our findings, raise policy considerations for decision makers such as police leaders and police complaints bodies, and outline implications for future research.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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 score0.769
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.342
Teacher spread0.301 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Community Safety and Well-BeingSame topicPolicing Practices and PerceptionsFrench-language works237,207