A Conversation about “Editing” Plurilingual Scholars’ Thesis Writing
Bibliographic record
Abstract
Drawing on our combined experiences providing thesis writing support, we critically consider the tensions surrounding policies and practices aimed at plurilingual graduate students using English as an additional language (EAL). Our trioethnographic methodology allows us to unpack and explore the ethics framing our individual “editing” practices amid institutional norms, expectations and ideologies. Drawing on relevant literature in the field, our conversations or “trialogues” produce insights and raise questions surrounding the ethical imperative of providing effective thesis writing support for plurilingual EAL writers in an era of increasing internationalization. We conclude with suggestions for flexible, targeted writing support that challenges narrow epistemologies and stale ideologies regarding taboo editing practices of academic and language literacy brokers involved in the production and revision of thesis writing.
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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 | 0.078 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.034 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".