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Record W7043434903

Self-determination as resistance to legal violence: Jurisdiction, property, and the geographies of conflict in Unistoten and Xolobeni

2021· article· en· W7043434903 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2021
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousAutonomyPoliticsResistance (ecology)SubalternCorporate governancePower (physics)Face (sociological concept)
DOInot available

Abstract

fetched live from OpenAlex

Indigenous peoples struggles of the right to self-determination are often framed as claims against a unified state. However, explanation of the forces inhibiting the expression of Indigenous self-determination should not settle with an understanding of the imposing power of the state. As I show, the realization of self-determination is undermined by the cumulative effects of legal practices and knowledges that contribute to the division of collective autonomies and disruption of their governance practices. In this transnational and comparative work on the emerging right to consent to resource extraction in South Africa and Canada, I argue that we might understand these dynamics by examining self-determination as a response to legal violence. I explain that legal violence in contemporary post-colonial conditions is expressed spatially through the categories of property and jurisdiction. One of the effects of conditions of legal violence is not only dispossession, but that Indigenous peoples assertions of self-determination and autonomy face settler counter-claims decrying internal conflict as evidence of subaltern political instability and inferiority. The analysis covers local contexts, national jurisprudence, and transnational norms related to the right to consent in both countries.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.912
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.026
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.139
Teacher spread0.136 · 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

Explore more

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