Bibliographic record
Abstract
Abstract Research-intensive universities in the global peripheries have begun to mount English for research publication purposes (ERPP) initiatives to increase plurilingual scholars’ publishing success. Though research into pedagogical initiatives is still limited, investigations of such programs can provide researchers with a greater understanding of the broader experiences and perspectives of scholars as well as the potential impact of interventions on course participants’ scholarly writing. Answering the call for more longitudinal work in ERPP, this article outlines a small-scale, qualitative investigation of the perceived impact of an intensive ERPP course at a Mexican university on two environmental scientists’ research writing five years following course completion. Data analysis included systematic review of participant CVs, as well as semi-structured interviews with two plurilingual EAL scientists and two ERPP practitioners connected to the ERPP course. Employing a critical plurilingual lens, this article discusses findings that not only outline the perceived impact of the intervention on these scientists’ research writing at different stages of their academic trajectories, but also highlight the plurilingual nature of their evolving scholarly practices. The article culminates with data-driven suggestions for plurilingual conceptualization and enactment of scholarly writing pedagogies, policies, and research agendas.
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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 | 0.068 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".