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Record W4403940989 · doi:10.18584/iipj.2024.15.2.14278

A Critical Discourse Analysis of the Evolution of Indigenous Education Policies in Colombia and Mexico and the Struggles for Decolonization and Pluriversality

2024· article· en· W4403940989 on OpenAlexvenueno aff
Mónica Pérez Marín, Sergio Cruz Hernández, Ilia Rodríguez

Bibliographic record

VenueInternational Indigenous Policy Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Cultures and Socio-Education
Canadian institutionsnot available
Fundersnot available
KeywordsDecolonizationIndigenousCritical discourse analysisPolitical scienceCritical theoryIndigenous educationSociologyGender studiesPoliticsLawBiology

Abstract

fetched live from OpenAlex

This research on Indigenous educational policies (IEPs) in the postcolonial histories of Colombia and Mexico focuses on: 1) identifying key normative concepts encoded in IEPs adopted in national policymaking, 2) analyzing how the evolution of normative concepts relates to political tensions between state, Indigenous, and international actors, and 3) discussing how the evolution of IEPs expose achievements and obstacles to decolonial struggles to advance Indigenous rights and pluriversal knowledges. A critical discourse analysis of laws and regulations adopted between1820 and 2020 in Mexico and Colombia shows a gradual and conflictive recognition of cultural and political rights, most recently through normative concepts like bilingualism, biculturalism, interculturalism, autonomy, and self-determination. These discursive shifts have operated, however, against the backdrop of ongoing political struggles of Indigenous peoples to demand that nation-states implement the agreements and principles enacted. This history of IEPs thus illuminates continuing tensions between the semiotic and material conditions of Indigenous communities as well as emerging practices of resistance and organization that may carry implications for further policy development.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.383
Teacher spread0.374 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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