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Record W3100920084 · doi:10.1177/1474474020970253

Articulating worlds otherwise: decolonial geolinguistic praxis, multi-epistemic co-existence, and intercultural education and development programing in the Peruvian Andes

2020· article· en· W3100920084 on OpenAlexfundno aff
Julian S. Yates, Justina Núñez Núñez

Bibliographic record

VenueCultural Geographies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPraxisScholarshipSociologyEpistemologyDecolonialityIndigenousPoliticsPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Focusing on key mediators of knowledge-exchange in the Andes – known as kamayoq – we explore a recursive politics of translation (historicized, power-laden processes of hierarchically ordering language and meaning). Focusing on intercultural and bilingual education and development programs in the Peruvian Andes, and connecting cultural geographical, anthropological, and critical socio-linguistic scholarship, we uncover how equivocations of Indigenous concepts reproduce a coloniality of knowledge and being. We explore how kamayoq re-purpose equivocations by reworking translations through Andean concepts and praxis, such as iskay yachay – a reciprocal dialogue among knowledges, which stresses epistemic multiplicity and diversity. We explore kamayoq praxis and iskay yachay as a decolonial geolinguistic praxis of articulating worlds (or ontologies) otherwise, in pursuit of multi-epistemic co-existence. Our findings raise questions about geographies of decolonial knowledges and praxis, particularly where potential decolonial praxis intersects with the formalized institutions of adult bilingual education and intercultural development programing.

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)0.007
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 score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.037
Scholarly communication0.0070.009
Open science0.0010.012
Research integrity0.0010.003
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 head0.090
GPT teacher head0.439
Teacher spread0.349 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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