Let’s Talk About Writing Support for Plurilingual Graduate Students
Bibliographic record
Abstract
Academic writing is an inseparable aspect of graduate school (Holmes et al., 2018) as students’ academic writing is the primary basis for assessment (Turner, 2011). The high-stakes nature of academic writing is magnified for plurilingual students, whose attendance at English medium universities is growing exponentially (Fenton-Smith & Humphreys, 2017). However, there is a scarcity of research that addresses how faculty support writing as an essential practice for plurilingual graduate students, particularly in English-medium universities where English is implicated in structures of power and privilege. Employing a critical analytic collaborative autoethnography (Anderson, 2006; Kempny, 2022) this research uses polyvocal conversations among seven researcher/practitioners to consider the question of how faculty members perceive and respond to the academic writing needs of plurilingual graduate students. Informed by intersectionality (Crenshaw, 2017; Hankivsky, 2014), these conversations illuminate the ways both educator identities and epistemological turns in education theory impact approaches to writing support for plurilingual graduate writers. Importantly, these discussions are implicated in the socio-political contexts of Canadian and Australian universities where systems of inequality act to marginalise plurilingual writers. These contextualised conversations then aim to problematise and revise existent, dominant deficit discourses and pedagogies of writing support for plurilingual students. Findings illuminate the capacity of educators, who are cognisant of their power and place, to generate alternative practices to support plurilingual graduate writers in service of more asset-orientated and inclusive spaces that take advantage of students’ plurilingual repertoires in English-dominant universities.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".