L2 Writing and L1 Composition in English: Towards an alignment of effort
Bibliographic record
Abstract
In North American university contexts, the language diversity found in English mainstream composition (“L1”) classrooms resembles more and more that found in ESL (“L2”) writing classrooms. As these two groups become less differentiated, those specifically trained in L2 writing might well wonder whether the needs of the non-native speakers of English are acknowledged and addressed in the mainstream classrooms. The author examines several different theoretical constructs that have informed and continue to inform the literature on L1 composition pedagogy, demonstrating that some of these allow for the inclusion of linguistically diverse groups better than others. Fortunately, the recent turn to social and critical approaches to teaching composition reflect well the preoccupations of both L1 and L2 writing teachers. More and more attention is being paid to discussions of “linguistic diversity,” a term which now includes non-native speakers. This suggests a future convergence in the activities of instructors of L1 and L2 writing, leading to benefits for linguistically diverse groups.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".