Thinking Languages in L2 Writing: Research Findings and Pedagogical Implications.
Bibliographic record
Abstract
This article reports the findings of part of a major study exploring the disciplinary writing processes and perceptions of 15 Chinese graduate students in sciences and engineering at a major Canadian university. The findings relate to the thinking languages of the participants in writing disciplinary assignments. The study reveals that whether an L2 writer thinks in L1 or L2 may not depend on one factor as proposed in earlier studies (Friedlander, 1990; Qi, 1998), but on a number of factors including the language of knowledge input, the language of knowledge acquisition, the development of L2 proficiency, the level of knowledge demands, and specific task conditions. It is the interplay among these (and possibly other) factors that determines the writer's choice of the thinking language, which may switch back and forth between L1 and L2. Further, although translation may be a positive strategy for a student with limited L2 proficiency, it may gradually phase out as the student thinks more in L2 and writes L2 in approximation to the language of native writers. Thus a thinking language continuum may exist along which the use of translation varies.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".