“Our country has gained independence, but we haven't”: Collaborative translanguaging to decolonize English language teaching
Bibliographic record
Abstract
Abstract The colonial history of many English language teaching (ELT) contexts has shaped how the concept of language is understood, how language policies are constructed, and how language education is organized. Various aspects of ELT in countries that were colonized continue to promote the imperialism of English (Motha, 2014) through the naming (i.e., labeling of linguistic phenomena as distinct languages, dialects, and language varieties), separation and hierarchization of languages, and the dominance of monolingual policies and practices in the classroom. Translanguaging, a theory and pedagogy that challenges colonial understandings of language and monoglossic norms in language teaching, has the transformative potential to liberate language practices that have been rendered invisible by abyssal thinking in ELT (García et al., 2021). Translanguaging as a theory posits that multilingual learners do not possess two or more autonomous language systems but rather that they select and deploy linguistic features from a unitary linguistic repertoire (Vogel & García, 2017). Translanguaging as a pedagogy urges educators to leverage learners’ entire linguistic and semiotic repertoires to support their learning instead of requiring them to keep certain languages outside the classroom. However, in educational contexts that respond to socially and politically imposed boundaries between languages, there are ideological and systemic challenges to the enactment of translanguaging as a pedagogy. This paper discusses these challenges with reference to the Malaysian language education context and draws on data from a collaborative translanguaging pedagogy designed through teacher-researcher collaboration and implemented in two Malaysian elementary English classrooms to offer recommendations for how ELT can be decolonized.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".