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Record W2953323737 · doi:10.29173/alr2524

Reconciliation Through Relationality in Indigenous Legal Orders

2019· article· en· W2953323737 on OpenAlexfundvenueaboutno aff
Alan Hanna

Bibliographic record

VenueAlberta Law Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaLaw Foundation of British Columbia
KeywordsIndigenousJurisprudenceFirst nationLawSociologyScholarshipPolitical scienceState (computer science)

Abstract

fetched live from OpenAlex

Canada’s reconciliation with Indigenous peoples and groups in Canada is an ambitious goal with little in the way of clear direction. Canadian courts have provided limited direction in their decisions, yet the result of litigation has imposed a concept of reconciliation based on First Nations remaining subordinate to state authority and interests. Reconciliation will be confounded without gaining a shared understanding with Indigenous peoples. Different Indigenous groups will have their own interpretation of what reconciliation may require to be successful. One approach to seeking common understandings is for Canadians to learnhow relationality operates as a function of disparate Indigenous legal orders. While substantive research into Indigenous legal orders is relatively new in Canadian scholarship, there is much knowledge to be gleaned from interdisciplinary research, particularly in anthropology, from the early twentieth century. At the risk of presenting an abrupt shift in disciplinary paradigms in this article, the author follows a thread of relationality from Canadian courts through the lens of doctrinal jurisprudence into relationality within various Indigenous legal orders through anthropological study. Combined, the article offers a potential path to reconciliation through relationality within Indigenous legal orders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.327
Teacher spread0.307 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2019
Admission routes3
Has abstractyes

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