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Record W2966131801

Broken Trust: Finding Our Way Out of the Damaged Relationship Through the Rebuilding of Indigenous Legal Institutions

2017· article· en· W2966131801 on OpenAlexaffabout
Aimée Craft

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIndigenousSovereigntyColonialismGenocideIndigenous rightsPolitical scienceLawSociologyConstitutionRacismHuman rightsPoliticsEnvironmental ethics
DOInot available

Abstract

fetched live from OpenAlex

The rights of Indigenous people in Canada are enshrined in the Constitution and supported by imperatives of and the honour of the Crown. Internationally, the rights of Indigenous people are confirmed in declarations and conventions that acknowledge the sovereignty of Indigenous people in their home territories. However, in Canada, these rights are cast under the shadow of a long history of colonization and colonialism, racism and prejudice, assimilation and cultural genocide. Reflections on this dark history and broken trust between Canada and Indigenous people were revisited by some, while Canada celebrated 150 years of Confederation. Many lamented the broken trust that continues to shape relationship. This chapter is a reflection on the era of reconciliation in which we find ourselves. The substance and scope of remains contested by many Indigenous people who argue that genuine must be anchored in revitalization, resurgence, resistance, and reclamation, through grounded normativity and practices that revalue Indigenous ways of knowing and being. This includes living and rebuilding Indigenous legal traditions which are embedded in profound understandings of relationship.

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)0.009
metaresearch head score (Gemma)0.016
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.869

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0440.082
Scholarly communication0.0190.020
Open science0.0030.017
Research integrity0.0040.012
Insufficient payload (model declined to judge)0.0040.001

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 head0.074
GPT teacher head0.366
Teacher spread0.292 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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