Manifestations of translanguaging and transknowledging in the assemblage of EAP writing
Bibliographic record
Abstract
Abstract This paper examines how translanguaging and transknowledging manifest in the research-paper writing process of pre-university international students as English language learners in a Canadian EAP program. A wealth of research has documented the challenges these students face in learning the conventions of English academic writing. More recent research has explored the potential of translanguaging in writing pedagogy; however, we argue that current research has yet to fully enact the decolonizing agenda underlying translanguaging as a movement, that is, to dissolve monolingual ideologies and hegemonic structures and to allow for transknowledging that represents the diverse ways of knowing circulating within the EAP classroom. Drawing on the concept of assemblage, data from one student composing a source-based research paper is presented to highlight the presence of translanguaging and transknowledging in their work, as well as the threat of transgression that demarcates which languages and knowledge are permitted. We conclude with methodological and pedagogical recommendations to disrupt conventional logic in how translanguaging research and practice can serve to create more globally inclusive educational spaces.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".