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Record W2760479966 · doi:10.5539/ass.v13n10p43

Cultural Empowerment and Language: Teaching Spanish to the Socially Disadvantaged Amazigh Population through the Alehop Programme

2017· article· en· W2760479966 on OpenAlexvenueno aff
Elvira Molina-Fernández, Fernando Barragán Medero, David Pérez‐Jorge, Francisco Oda Ángel

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Linguistics, Cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedEmpowermentAutonomyPedagogyContext (archaeology)SociologyDiversity (politics)Cultural diversityPsychologyMathematics educationPolitical science

Abstract

fetched live from OpenAlex

This article describes an action research programme designed to resolve classroom problems, in preschool and primary education, related to the use of Spanish as a hegemonic language in a bilingual context in which students are from the Amazigh culture. The Alehop programme aims to motivate students to learn and the results demonstrate that this classroom innovation is possible. Moreover, the use of everyday life situations and typical problems helps and favours enquiry-based learning. Intercultural school life without violence is shown to be possible. Strategies include giving voice to the students, addressing relevant social issues, and creating an environment of trust and collaboration. An evaluation of the results validates a methodology that encourages cultural and linguistic diversity, and points to a need to respect the autonomy, freedom and human rights of students in politically and economically disadvantaged conditions. Empowerment is linked to the autonomy of students and teachers.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.326
Teacher spread0.298 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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