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Record W2468882050 · doi:10.18224/educ.v19i1.5022

Analysis of the Educational Aims of Language Teaching in Chile: the ideology behind curriculum adjustments

2016· article· en· W2468882050 on OpenAlexaff
Rocío Esquivel

Bibliographic record

VenueRevista Educativa - Revista de Educação · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsIdeologyCurriculumAppropriationEmancipationSociologyLanguage educationPoliticsGovernment (linguistics)PedagogyMathematics educationEpistemologyPolitical scienceLinguisticsPsychologyPhilosophyLaw

Abstract

fetched live from OpenAlex

Abstract : his research aims to present the social, political, epistemological and pedagogical foundations of past three language curriculum’s adjustments in Chile, and the methodology used for this purpose. We assume that the study of didactic of language can not be limited to a technical analysis of its teaching. By analyzing the requirements of the Chile’s curriculum of language, we discovered its educational purposes and the ideology that inspires them.This ideology has been introduced under the form of adjustments, that according to the government does not mean changes in the fundamentals of language teaching. He reflection on the aims proposed in the language curriculum will give us a better understanding of the present and past of Chilean society, its collective imaginary and values ​​that will shape future generations, as well as calling into question the role of language’s teaching as a tool of emancipation and appropriation of the world. Keywords: Curriculum; Language teaching; Ideology and educational objectives.

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)0.008
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 score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.368
Teacher spread0.346 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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