A systemic functional linguistic approach to usage-based research and instruction
Bibliographic record
Abstract
Abstract The present chapter illustrates how Systemic Functional Linguistics (SFL) can inform a usage-inspired approach to researching and teaching L2 writing in a postsecondary context. We first outline an SFL perspective to multilingual academic literacy development and then illustrate this perspective by means of longitudinal, corpus data on nominalization use in the English academic writing of francophone university students over four years. By means of quantitative indicators (nominalization frequencies, erroneous forms, measures of L2 proficiency scores and syntactic complexity) and qualitative analyses (of the discourse functions that nominalization serve), we argue that French-speaking writers’ use of nominalization in English indexes both language-specific and language-interdependent aspects of multilingual academic literacy development. We conclude with implications for further SFL-informed research and instruction that aims to promote multilingual academic literacy development by raising crosslinguistic awareness of the forms and functions of nominalization in academic discourse.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".