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Record W2414777334 · doi:10.1080/07329113.2016.1157377

Aboriginal healing lodges in Canada: still going strong? Still worth implementing in the USA?

2016· article· en· W2414777334 on OpenAlexaboutno aff
Marianne O. Nielsen

Bibliographic record

VenueThe Journal of Legal Pluralism and Unofficial Law · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSovereigntyPoliticsIndian countryAppropriationState (computer science)Culturally appropriateLawRecidivismIntervention (counseling)Political scienceIndigenous rightsPublic administrationSociologyMedicineCriminologyNursingGerontology

Abstract

fetched live from OpenAlex

Aboriginal healing lodges are a means of accommodating Aboriginal customary law in the Canadian correctional system by providing holistic culturally appropriate services to Aboriginal offenders. They combine Aboriginal healing practices with non-Aboriginal correctional practices as determined by Canadian law and Correctional Service Canada policy. Some are operated by Aboriginal Nations/organizations and some by the Correctional Service Canada. The co-optation of healing lodges through federal correctional policy is contrary to the intent of Canadian law. This combination of state disregard for the law and cultural appropriation provides lessons for Indigenous American groups contemplating more involvement in correctional services. In the USA, the Tribal Law and Order Act of 2010 and policies regarding private prisons may help or threaten the implementation of healing lodges. In both countries, state laws and policies seem to be subverting Indigenous sovereignty but this makes healing lodges even more valuable. Just as healing lodges could enhance community capacity building in Canada, Indigenous American healing lodges could assist with community capacity-building, as well as reducing recidivism rates. Healing lodges thereby have the potential to increase indigenous sovereignty in both countries, political climate permitting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.305

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 studies0.0000.000
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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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
Published2016
Admission routes1
Has abstractyes

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Same venueThe Journal of Legal Pluralism and Unofficial LawSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207