Words that don’t translate: investing in decolonizing practices through translanguaging
Bibliographic record
Abstract
Drawing on the pedagogical framework of critical multilingual language awareness, this article demonstrates how the production of a YouTube video explaining lexical gaps can help language learners construct a translanguaging space and invest in decolonizing practices. Based on a study examining the language and literacy practices of university students in Hong Kong, it explores how English majors enrolled in a communications course created three-minute videos explaining Cantonese words that do not have equivalent terms in English. By explaining these translational gaps, learners were able to not only reflect on their languages and cultures, but also articulate a cognitive and affective awareness of the way language works. They were able to initiate translanguaging practices that displaced the privileged position of English and enabled them to resist colonial ways of knowing. Learners reframed their identities as knowledgeable experts who had the authority to speak confidently about their L1, while the non-Cantonese speaking instructor became learner and listener. By reconfiguring relations of power, learners were able to initiate and invest in decolonizing practices that asserted their identities as legitimate, multilingual speakers and enabled them to claim the right to speak.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".