MétaCan
Menu
Back to cohort
Record W2805972573 · doi:10.5296/jei.v4i1.12849

English as a “Killer Language”? Multilingual Education in an Indigenous Primary Classroom in Northwestern Mexico

2018· article· en· W2805972573 on OpenAlexaff
María Rebeca Gutiérrez Estrada, Sandra R. Schecter

Bibliographic record

VenueJournal of Educational Issues · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsYork University
Fundersnot available
KeywordsNexus (standard)IndigenousVariety (cybernetics)Context (archaeology)Participant observationLanguage planningLanguage policyIndigenous languageBilingual educationPedagogyEthnographyAgency (philosophy)Foreign languageSociologyPolitical scienceGeographySocial scienceEngineeringAnthropologyComputer science

Abstract

fetched live from OpenAlex

We report findings of an ethnographic study that explored complexities of English Language Teaching (ELT) in a minority indigenous context in northwestern Mexico. The study investigated a trilingual education setting at the nexus of 2 major events: incorporation of Intercultural Bilingual Education throughout Mexico and integration of ELT into the country’s public school system. Methods included participant observation in primary-level language classes and semi-structured interviews with educators and other stakeholders affiliated with a rural school where an indigenous variety, a societal variety, and a foreign language were taught. Findings indicate that teacher agency was a powerful tool in linguistic and cultural maintenance and transforming language policy and planning at the local level. Although the spread of English may be unavoidable, with local community involvement and a school-based commitment to support linguistic and cultural maintenance, the micro language policy context can be conFigured to promote a symbiotic relationship among linguistic varieties.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.207

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 studies0.0080.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.014
GPT teacher head0.324
Teacher spread0.310 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational IssuesSame topicSecond Language Learning and TeachingFrench-language works237,207