Emergent Identities and Representations in ELT in Minority Language Contexts in Northern Mexico
Bibliographic record
Abstract
In the early 2000s, Mexico experienced a series of changes modelled after UNESCO’s guidelines for intercultural education, which necessitated the establishment of the General Office of Intercultural Bilingual Education. This institution aims to provide indigenous students with education that acknowledges their cultural identity, language, knowledge, and attitudes. Another parallel change occurring in Mexico stemmed from the North American Free Trade Agreement (NAFTA) with Canada and the United States. NAFTA impacted Mexican educational policy by incorporating the teaching of English at the state and national levels beginning in public elementary schools. \nAdopting an interdisciplinary approach, this dissertation explores the role of English Language Teaching (ELT) in a community in northwestern Mexico where a minority language – Mayo – was taught alongside English. The research also seeks to unfold the discourse(s) embodied in the representation of minority students and languages in Mexico through the analysis of policy documents used in Intercultural Bilingual Education (IBE) programs, as well as in the attitudes of the teachers and stakeholders involved in them. I also look at representations of indigenous people in two texts designed by IBE for the Mayo community. \nThis dissertation incorporates an ethnographic study that uses participant observations in Mayo and English language classes and semi-structured interviews with teachers and stakeholders attached to a rural school in northwestern Mexico as primary data collection strategies. Policy documents and curricular materials are analyzed using the tools of Critical Discourse Analysis. \nThrough qualitative data analysis, we discover how the nation-state instantiates a particular Mexican identity and perpetuates stereotypes of indigenous minorities through policy documents and curricular texts. At the same time, the data obtained through ethnographic fieldwork indicate that teacher agency was a powerful tool in linguistic and cultural maintenance and in transforming language policy and planning at the local level, such that Mayo and English were positioned equally in the curriculum.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".