Trans/plurilingual Pedagogies: A Multiethnography
Bibliographic record
Abstract
Trans/plurilingual theory and pedagogies have been generating extensive attention within global English language teaching and teacher education. This article responds to the burgeoning area of trans/plurilingual pedagogies, outlining diverse pedagogical practices and perspectives from a group of English language educators at a large, cosmopolitan Canadian university. Considering recent assertions that there are onto-epistemological differences between multilingualism, plurilingualism, and translingualism, this article looks to demonstrate how (or even if) these differences manifest in the pedagogical practices of diverse faculty within the same language teaching and teacher education programs. Drawing on multiethnographic data, this article concludes with a discussion of the potential and limitations of critical pedagogies, the affordances of multiethnography as an accessible methodology for use by researchers and pedagogues, and a call for greater bi-directional knowledge flow between language researchers and classroom instructors.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".