The Revision Patterns and Intentions in L1 and L2 by Japanese Writers: A Case Study
Bibliographic record
Abstract
This case study investigated the revising patterns and intentions in L1 and L2 of Japanese writers with various writing experiences. Three participants were selected through purposeful sampling to do within-case comparisons. One participant was an experienced writer in both Japanese and English; one was an experienced writer in Japanese, but not in English; the other was an inexperienced writer in both Japanese and English. Using think-aloud protocols, these participants produced two revised essays in Japanese and two revised essays in English. The revised texts, think-aloud protocols, and retrospective interviews were analyzed to identify revision patters and revision intentions. first, it was found that all three writers produced many more revisions in Japanese than in English. Second, it was found that these writers showed similar revising intentions across languages. These findings were interpreted in terms of revision control structure, which is gained through writing experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".