Second Language Graduate Students’ Experiences at the Writing Center: A Language Socialization Perspective
Bibliographic record
Abstract
The writing center is a common form of academic writing support in Canadian and U.S. universities (Moussu & David, ). With its nonproofreading policy, some scholars have indicated the service may be less effective for international students who require more explicit assistance on surface‐level features in their academic writing (Harris & Silva, ; Myers, ). However, these discussions often omit or insufficiently address international graduate students as a distinctive population (Phillips, ). To address this gap, this article presents results from two complementary case studies involving the use of writing centers by three second language (L2) Chinese graduate students (two doctoral and one master's) at a research‐intensive Canadian university. Drawing on a second language socialization theoretical framework (Duff, ; Zuengler & Cole, ), the researchers examine the role of the university's writing center in the participants’ enculturation into academic discourses, practices, identities, and communities. Data indicate that international graduate students spend considerable time and effort seeking out writing support to improve academic practices. Only the master's student was able to make full use of the writing center tutorials due to her strategic socialization of the tutor. Implications are provided to minimize student burden and maximize specialized writing support for L2 graduate students.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".