Bibliographic record
Abstract
This study examines the trajectories of two multilingual, racialized academic writing faculty, presenting how we brought our Southern onto-epistemologies (e.g., Santos, 2016) to curriculum, teaching, and assessment. Although plurilingualism has become a significant dimension of Canadian higher education (Marshall, 2020), monolingual norms that emphasize native-like competence continue to be a mainstream discourse in many academic writing courses. Building on the recent raciolinguistic critique (Rosa & Flores, 2017) of the lack of discussion of racism in academic literacies discourse, we acknowledge that academic literacies continues to force plurilingual, international students into a white subject position. Acknowledging the tension between the monolingual ideal and multilingual realities, we explore how two plurilingual, non-white faculty challenge an academic writing tradition that is constructed by the white listening subject. By co-creating duoethnographic narratives that provide insight into our complex biographical journeys as cycles of becoming (Thibault, 2020), our story shows how teaching academic writing is not simply teaching a skillset but involves constant negotiation between students’ and teachers’ lived experiences. Through this process, we conceive of teaching academic literacies as both an ideological construct and a multisemiotic process that involves multiple histories and meaning-making resources across diverse time and place scales.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".