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Record W2774451565 · doi:10.1163/22134514-00404001

Tribal Courts, Restorative Justice and Native Land Claims

2017· article· en· W2774451565 on OpenAlexaboutno aff
Zia Akhtar

Bibliographic record

VenueEuropean Journal of Comparative Law and Governance · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLawJurisdictionRestorative justicePolitical scienceSovereigntyFederal jurisdictionContext (archaeology)TreatyEconomic JusticeDevolution (biology)Government (linguistics)Eminent domainSociologyGeographyPolitics

Abstract

fetched live from OpenAlex

The Native American tribes in the United States have maintained distinctive customs which they practice within their ‘eviscerated’ sovereignty. The tribes exercise their jurisdiction as ‘sovereign’ nations under devolution of their lands granted by the federal government, which still has a right of preemption and the power of alienation. The tribal courts exercise the restorative justice principles that are integral to their judicial procedures and where the emphasis is on healing. The disputes in tribal courts are settled by mediation through Peacekeeping Circles that restore the parties to the pre-trial status and there is input from elders in the community. The Native people not only have to differentiate and preserve their justice framework, but also claim title to land where it has been extinguished by treaty, eminent domain or Executive order of the us government. The argument in this paper is that the restorative justice principle is part of the customary law of the tribes in the us and in Canada, and their dormant land claims can be revisited if this judicial process is maintained in the context of sustaining their customs within the federal legal framework.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.035
Scholarly communication0.0080.006
Open science0.0020.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.067
GPT teacher head0.350
Teacher spread0.283 · 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
Published2017
Admission routes1
Has abstractyes

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