The fall of bilingualism: Teacher candidates’ voices on the implementation of critical plurilingualism in English language teaching
Bibliographic record
Abstract
Plurilingualism is an inclusive language teaching approach to sustain multilingual societies, but there is little investigation on teacher candidates’ (TCs) beliefs and challenges before and after its implementation. This interpretive qualitative study introduced plurilingualism in teacher education at a Canadian university. Sixteen TCs participated in the study, which 1) investigated TCs’ conceptualizations of plurilingualism, and 2) examined TCs’ perceptions of overall affordances of critical plurilingual pedagogies before and after their practicum. For four months, participants experimented with plurilingual pedagogies such as translanguaging and cross-linguistic analysis, designed tasks, and taught lessons. Five types of data were generated: 1) weekly annotations of readings on Perusall, 2) designed language tasks, 3) task delivery demonstrations, 4) lesson plans, and 5) final reflection after the teaching practicum. Inductive content analysis was conducted on NVivo with data triangulation. Findings suggest that TCs shifted their views of language, and aligned plurilingual pedagogies with equity, diversity, inclusion and decoloniality principles. Findings also show that TCs transgressed the monolingual discourses often present in schools, and felt empowered after the training. We call for the inclusion of critical plurilingual practices in teacher education programs for the sustainability of multi/plurilingualism.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".