Autonomous Pluralistic Learning Strategies Among Mexican Indigenous and Minority University Students Learning English
Bibliographic record
Abstract
This critical ethnographic case study draws on Indigenous and minority students’ process of learning English as a Foreign Language (EFL) in Mexico. The study specifically focuses on students who enrolled in a program called A wager with the Future. The aim of the study is to identify and understand contributing factors in these students’ struggles with the process of learning English by focusing on factors that influence their investment in EFL. The research is framed by (critical) applied linguistics and post-colonial theories that favour the integration of an understanding of these students’ socio historical context in their learning of English, and question (unequal) power relationships between languages and cultures in Mexico. The methodology was designed to ensure trustworthiness by adopting multiple data collection techniques, and to decolonize the research process by using participatory methods that featured researcher/participant co-analysis of the data. On a macro level, findings show that students enrolled in the program experience a relationship with English that is rooted in Mexico’s colonial legacies (as expressed through discrimination in the EFL classroom), which has an impact on their subjectivities; specifically, they feel afraid and inferior in the EFL classroom. On a micro level, the programming adopted in the university’s Language Department does not draw on diverse students’ multi-competences in other languages. Nonetheless, some Indigenous students manage to invest in EFL by creating imagined communities, and appropriating English through the creation of autonomous pluralistic language learning strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".