Bibliographic record
Abstract
Learning to write in English for academic purposes presents a significant challenge for non-native speakers. Not only must they deal with the obvious linguistic and technical issues such as syntax, vocabulary, and format, but they must also become familiar with Western notions of academic rhetoric. (West or Western in this article refer primarily to North America.) Collisions of cultures are experienced when the discourse practices L2 writers are expected to reproduce clash with what they know, believe, and value in their L1 writing. For this article I reviewed a range of literature that addresses writing and culture. Described by researchers and by L2 writers are collisions regarding voice, organization, reader/ writer responsibility, topic, and identity. Implications for writing pedagogy include awareness of contrastive rhetoric on the part of ESL writing instructors; instructors' acknowledgment of and appreciation for the prior knowledge that students bring from their L1; realization on the part of ESL writing instructors that Western notions of,for example, voice are indeed just notions and are simply one way among many of expressing oneself; and a need for open discussion with students about how they might incorporate standard Western notions of writing without compromising their own identity.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".