Bibliographic record
Abstract
Genre analysis has become an important tool for teaching writing across the disciplines to non-native English-speaking (EL2) and native English-speaking (EL1) graduate students alike. Since the pressing needs of EL2 graduate students have meant that educators often teach them in separate classes, and since genre-based research into teaching higher-level writing has been largely generated in fields such as English for Academic Purposes, we have an insufficient understanding of whether this instructional mode plays out similarly in EL1 and EL2 classrooms. Launching a genre-based course on writing research articles in parallel sections for EL1 and EL2 graduate students provided an opportunity to address this knowledge shortfall. This article qualitatively examines the different classroom behaviors observed in each version of the course when a common curriculum was used and specifically explores three key themes: initial receptivity, nature of student engagement, and overall assessment. Our study shows that although EL2 and EL1 learners have similar needs, the obstacles to their benefitting from genre-based instruction are different; EL2 students must learn to identify themselves as needing writing support that transcends linguistic matters, while EL1 students must learn to identify themselves as needing writing support despite their linguistic competence. Providing the same mode of instruction can benefit both populations as long as educators are sensitive to the specific challenges each population presents in the classroom. The insights gained contribute to the scholarship on genre-based teaching and offer ways of better meeting the needs of EL1 and EL2 students alike.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.069 |
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".