Bibliographic record
Abstract
Paul Kei Matsuda (1999) has written about the divide between U.S. composition and applied linguistics, which he attributes to an institutionalization of the division of labour between Applied Linguistics and composition in the early 1960’s. As such, when language concerns resurfaced in composition in the early-2000s, this division of labour led to a “lack of a community of knowledgeable peers who [could] ensure intellectual accountability” among compositionists (Matsuda 2013). Did this same divide occur in a Canadian context, or has the field of second language writing developed differently in Canada? The goal of this paper is to construct a history of L2 writing scholarship, reading for any collaboration with writing studies as both fields “grew up” together in Canada. To this end, the paper extends the work of Alister Cumming who narrates the evolution of L2 writing scholarship in Canada. Using data from Cumming’s “Studies of Second-Language Writing in Canada: Three Generations,” this paper reports findings from archival research that traces the publication history of key knowledge-workers (identified by Cumming) from the 80s to the 2000s. These findings tell a story about how L2 writing developed as a field in Canada and the ways it was influenced by fields like education and applied linguistics. Ultimately, these findings contribute to the broader narrative of how L2 writing has professionalized in Canadian higher education. By investigating the historic formation of L2 writing in Canada, scholars, writing instructors, and writing program administrators can draw on historic relations to create writing pedagogy that best meets the needs of an increasingly linguistically diverse writing classroom.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".