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Record W2602182565 · doi:10.22329/wyaj.v33i1.4811

WANISKĀ: REIMAGINING THE FUTURE WITH INDIGENOUS LEGAL TRADITIONS

2017· article· en· W2602182565 on OpenAlexaffvenueabout
Hadley Friedland

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIndigenousNarrativePower (physics)SociologyLawCommissionPolitical scienceAestheticsEnvironmental ethicsPhilosophyArtLiterature

Abstract

fetched live from OpenAlex

With the release of the Truth and Reconciliation Commission’s final report, which stressed the revitalization of Indigenous legal traditions is essential to reconciliation, we are potentially at the cusp of a historical turning point in Canada. As momentum around the revitalization of Indigenous laws grows, this raises many important questions for the future. Can we collectively imagine a Canada where Indigenous law is integrated and in use? What would, or should, this respectful relationship look like? This article explores these questions through narrative. Narrative, as many Indigenous and non-Indigenous thinkers have identified, has unique capacity to create space for conversations, spark imagination, and let us contemplate the incomprehensible. This article mindfully uses narrative as a means to vulnerably re-imagine a future relationship between Indigenous and other legal traditions in Canada. It acknowledges the deep-rooted enduring power of Indigenous laws, as well as both the immensity and transitory nature of current complexities. It names aspects of learning and engagement with the Cree legal tradition the author may never fully comprehend, but still senses are important. It grapples with the enormity of hope and despair, the power of violence and the power of love. It argues, through narrative, that law is living, time is fluid, change is possible and our shared future is ours to re-imagine.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.050
GPT teacher head0.350
Teacher spread0.299 · 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.

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

Citations1
Published2017
Admission routes3
Has abstractyes

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