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Record W2800000445 · doi:10.3138/utlj.2017-0062

Legal pluralism? Indigenous rights as legal constructs

2018· article· en· W2800000445 on OpenAlexvenueno aff
Chris Thornhill, Carina Calabria, Rodrigo Céspedes, Dominic Npoanlari Dagbanja, Elizabeth O’Loughlin

Bibliographic record

VenueUniversity of Toronto Law Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Peoples' Rights and Law
Canadian institutionsnot available
Fundersnot available
KeywordsLegal pluralismIndigenous rightsIndigenousLawPolitical scienceFundamental rightsReservation of rightsInternational human rights lawLegal researchHuman rightsLegal cultureLegal realismCitizenshipSociologyRight to propertyLaw and economicsPolitics

Abstract

fetched live from OpenAlex

This article sets out a sociological critique of the theories of legal pluralism that underpin contemporary constructions of Indigenous rights. It argues that pluralistic theories of Indigenous rights, which see Indigenous rights as challenging the legal orders of nation-states, are based in simplified analyses of the construction of such rights, and they fail to account for the subjects that lay claim to indigeneity and the relation between such subjects and national legal systems. To explain this, the article reconstructs primary patterns of Indigenous rights law in global international law, regional international law, and municipal law in Latin America and Africa. Against standard pluralistic approaches, this article argues that we need to adopt a sociological model based in world citizenship, which depends on relatively autonomous legal functions, to understand the growth of Indigenous rights. Central to this model is the claim that the imputation of Indigenous rights extends the reach of domestic legal systems and that it is closely linked to a process in which the national legal system, supported by global legal norms, penetrates more deeply and more inclusively into national society.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.995

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 studies0.0060.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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 designNot applicable
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

Citations7
Published2018
Admission routes1
Has abstractyes

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