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Record W2510774530 · doi:10.35502/jcswb.8

Justice on Turtle Island: Continuing the evolution of policing with First Nations, Métis and Inuit Peoples in Canada

2016· article· en· W2510774530 on OpenAlexvenueaboutno aff
Robert Chrismas

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousMainstreamEconomic JusticeRestorative justiceCriminologyIntervention (counseling)Political scienceTurtle (robot)Conflict resolutionCriminal justiceSociologyLawEcologyPsychology

Abstract

fetched live from OpenAlex

The relationship between policing and Canada’s First Nations and Métis peoples has historically been strained, and these tensions continue trans-generationally. This social innovation paper explores the possibility of integrating two effective paradigms that might positively enhance the relationship between policing and First Nations, Métis and Inuit peoples of Canada. The first is increased multi-sectoral collaboration around social issues, based on proven models such as Prince Albert Saskatchewan’s community mobilization initiative. The second is finding culturally sensitive alternatives to criminal courts by diverting cases into restorative justice processes that resonate more closely with Indigenous beliefs. These approaches would focus more on restoring community balance than pitting adversaries against one another in the mainstream criminal courts. Proposed for consideration is widening the restorative justice circle to include multi-sectoral resources to reduce the chances of re-offending and enhance conflict intervention and resolution.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.090
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0260.008
Scholarly communication0.0060.001
Open science0.0030.006
Research integrity0.0010.003
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 head0.012
GPT teacher head0.274
Teacher spread0.262 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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