Translanguaging in L2 Writing: A Critical Analysis of Current Research and Emerging Directions
Bibliographic record
Abstract
This paper critically reviews current research on translanguaging in second language (L2) writing, focusing on how multilingual learners use their full linguistic repertoire during the writing process. It explores key strategies, such as back translation, rehearsing, and drafting in L1, that support idea generation, vocabulary development, and problem-solving across writing stages. While translanguaging fosters metalinguistic awareness and inclusivity, it also faces challenges from institutional norms favoring monolingualism. The paper identifies research gaps in areas such as advanced academic writing, digital tool integration, student agency, and long-term outcomes. It calls for further studies that reconcile theory with practice and support translanguaging as a legitimate and effective approach to L2 writing in diverse educational contexts.
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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 | 0.047 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".