âWhat Makes Children Different Is What Makes Them Betterâ: Teaching Mexican Children âEnglishâ to Foster Multilingual, Multiliteracies, and Intercultural Practices
Bibliographic record
Abstract
This dissertation documents a critical-ethnographic-action-research (CEAR) project conducted in two elementary schools in Oaxaca, Mexico, with the collaboration of one language teacher educator and ten language student teachers. The two schools have a diverse student body composed of mestizo children and children from different Indigenous groups. The CEAR Project challenged historical and societal ideologies that position Indigenous children as deficient learners and their translanguaging and multiliteracies practices as inappropriate for schools. The CEAR Project was also a response to a world phenomenon that associates English with “development” and economic success and Indigenous and “minoritized” languages with backwardness marginalization. \nThe CEAR Project’s purpose was to use the student teachers’ English language praxicum in order to: (a) develop elementary school teaching expertise, (b) co-construct affirming identities among all the participants, (c) foster multilingual, multiliteracies, and intercultural practices, and (d) dialogue with the children in order to change pejorative ideologies that regard certain languages, literacies, and cultures as better than others. The Transformative Multiliteracies Pedagogy developed by Cummins (in press) and critical pedagogies theory (Freire, 1970; Norton & Toohey, 2004) informed the CEAR Project and the data collected through classroom observations, semi-structured interviews, and children’s work samples. \nUsing narrative, photos, and videos, this dissertation presents the migratory lives, the families, and the language and literacy practices of 50 children, and their views regarding the English language and Indigenous languages and peoples. It portrays the vivid critical moments and changes that occurred in the praxicum as the children became teachers and linguists. Through the construction of identity texts and the translanguaging and multiliteracies practices that the student teachers and the children engaged in, stories emerge that portray them as the intelligent, creative, and genuine individuals that they really are. This dissertation also documents how the children’s complex lives challenged constructs such as “family” and “Indigenous,” and the new Mexican educational policy that brings English into public elementary schools using a generic English software. It is concluded that every policy, theory, social construct, pedagogy, and curriculum should be challenged on a daily basis if we are truly to serve the ever-evolving diverse classrooms of today.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".