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Record W3012311259 · doi:10.25222/larr.213

Self-Governance in Bolivia’s First Indigenous Autonomy: Charagua

2020· article· en· W3012311259 on OpenAlexfundno aff
Nancy Postero, Jason Tockman

Bibliographic record

VenueLatin American Research Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Society in Latin America
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of California, San Diego
KeywordsIndigenousAutonomyPoliticsNegotiationPolitical scienceIndigenous rightsGovernment (linguistics)Corporate governanceDeclarationPublic administrationSelf-determinationPolitical economyLawSociologyManagementEconomics

Abstract

fetched live from OpenAlex

The UN Declaration on the Rights of Indigenous Peoples recognizes Indigenous peoples’ rights to self-determination and to maintain their distinct institutions. This article investigates how those rights are being exercised in Charagua, which became Bolivia’s first “Indigenous autonomous government” when the municipality’s Guaraní majority approved conversion in 2015. We explore the construction of novel institutions of self-government to assess how local Guaraní leaders are negotiating autonomy, both externally and internally. The result of those negotiations is a hybrid political system in which power is balanced between an executive organ (as required by Bolivian law) and a deliberative assembly (the Ñemboati Guasu, which operates according to Indigenous custom). The prominence of the assembly expresses a significant form of autonomy that promotes intercultural political participation and enacts Indigenous self-government in ways that are important to Guaraní people. Yet, because the new political unit does not control subsoil rights and thus cannot determine the sorts of development that take place in its territory, we cannot yet say the Guaranís are exercising full and robust autonomy as expressed by the UN Declaration’s provisions for self-determination.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.108
GPT teacher head0.418
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations26
Published2020
Admission routes1
Has abstractyes

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