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Record W4256317025 · doi:10.32920/ryerson.14668155

First occupants: the erosion of indigenous sovereignty through legal narratives

2021· preprint· en· W4256317025 on OpenAlexaboutno aff
Taylor E. MacLean

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
FundersChina Scholarship Council
KeywordsSovereigntyIndigenousAutonomyPoliticsNarrativeState (computer science)Self-determinationPolitical scienceLawIndigenous rightsSociology

Abstract

fetched live from OpenAlex

Land claim cases within Canada have yielded mostly small wins for Indigenous nations. While certain cases represent success in reinstating rights to cultural practices, granting certain levels of autonomy, and acknowledging rights to land use for culturally relevant activities, overriding sovereignty rests with Canada. Even land claim cases deemed successful are still adjudicated within the Canadian court system, and it is the nation-state of Canada that determines the validity of Indigenous claims to traditional territories. In this paper, I explore the discursive and narrative devices utilized within judicial rulings that uphold Crown sovereignty and deny Indigenous sovereignty. I argue that Indigenous sovereignty is undermined in legal discourse through the use of concealed narrative acts, which serve to sterilize racialized legal doctrines and distort the social and political history of relations between Indigenous nations and the Crown.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.571
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0190.042
Scholarly communication0.0140.006
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.316
Teacher spread0.281 · 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
Published2021
Admission routes1
Has abstractyes

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