A Tutor-Led Collaborative Modelling Approach to Teaching Paraphrasing to International Graduate Students
Why this work is in the frame
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Bibliographic record
Abstract
Language learners are at particular risk of being accused of plagiarism, and this is often due to incorrect paraphrasing and quoting practices. Tertiary institutions tend to provide rudimentary citation resources through their academic integrity initiatives. Handouts, webinars and one-hour workshops may be enough for undergraduate writers who receive more elaborate instruction and practice opportunities in their classes, but for international graduate students with little to no instruction on source use in their undergraduate degrees, these resources are not enough. These writers often need more conceptual and procedural clarity to paraphrase and use sourced information correctly in their writing. This article introduces a student-centred, collaborative modelling approach and a 5-step procedure for teaching paraphrasing to multilingual graduate students in one-to-one writing center tutoring sessions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it