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Record W56296821 · doi:10.29173/alr140

Restorative Justice at the Miyo Wahkotowin Community Education Authority

2011· article· en· W56296821 on OpenAlexaffvenueabout
Matthew Wildcat

Bibliographic record

VenueAlberta Law Review · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsRestorative justiceEconomic JusticeTransformative learningSociologyConflict resolutionCriminologySet (abstract data type)Political scienceLawSocial sciencePedagogyComputer science

Abstract

fetched live from OpenAlex

This article examines the development of restorative justice techniques at the Miyo Wahkotowin Community Education Authority, which operates three schools for the Emineskin Cree Nation in Maskwacis (Hobbema, Alberta). The article begins with an examination of conflict with Maskwacis. Contextualizing the conflict that takes place within the community is necessary to understand the restorative justice techniques employed by Miyo Wahkotowin. While many techniques of restorative justice has previously been implicitly practiced in the schools overseen by Miyo Wahkotowin, officially adopted restorative justice techniques provided a set of ideas that allows Miyo Wahkotowin to explicitly discuss and practice alternate forms of conflict resolution. The article ends by looking at how the use of restorative justice techniques by Miyo Wahkotowin holds transformative potential for the community of Maskwacis as a whole.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.788

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.009
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0020.002
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 head0.076
GPT teacher head0.371
Teacher spread0.295 · 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

Citations4
Published2011
Admission routes3
Has abstractyes

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