MULTICULTURALIDAD E INTERCULTURALIDAD: DESAFÍOS EPISTEMOLÓGICOS DE LA ESCOLARIZACIÓN DESARROLLADA EN CONTEXTOS INDÍGENAS
Bibliographic record
Abstract
El artículo aborda los conceptos de multiculturalidad, interculturalidad y educación intercultural, como perspectivas teóricas que permiten explicar la dinámica intercultural de la acción educativa y como desafío epistemológico de los conocimientos indígenas en la escolarización. Para ello se realiza un análisis de los elementos teóricos que sustentan estos conceptos; se problematiza la dificultad epistemológica de la educación intercultural, considerando el contexto en que se lleva a cabo, y en consecuencia, se plantea que la dinámica de estos conceptos permite la posibilidad de considerar la interculturalidad tanto para la formación como para la problematización de situaciones interculturales que están en proceso de investigación.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".