An Exploration of the Scientific Writing Experience of Nonnative English-Speaking Doctoral Supervisors and Students Using a Phenomenographic Approach
Bibliographic record
Abstract
Nonnative English-speaking scholars and trainees are increasingly submitting their work to English journals. The study’s aim was to describe their experiences regarding scientific writing in English using a qualitative phenomenographic approach. Two focus groups (5 doctoral supervisors and 13 students) were conducted. Participants were nonnative English-speakers in a Swedish health sciences faculty. Group discussion focused on scientific writing in English, specifically, rewards, challenges, facilitators, and barriers. Participants were asked about their needs for related educational supports. Inductive phenomenographic analysis included extraction of referential (phenomenon as a whole) and structural (phenomenon parts) aspects of the transcription data. Doctoral supervisors and students viewed English scientific writing as challenging but worthwhile. Both groups viewed mastering English scientific writing as necessary but each struggles with the process differently. Supervisors viewed it as a long-term professional responsibility (generating knowledge, networking, and promotion eligibility). Alternatively, doctoral students viewed its importance in the short term (learning publication skills). Both groups acknowledged they would benefit from personalized feedback on writing style/format, but in distinct ways. Nonnative English-speaking doctoral supervisors and students in Sweden may benefit from on-going writing educational supports. Editors/reviewers need to increase awareness of the challenges of international contributors and maximize the formative constructiveness of their reviews.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".