A Proposal for Critical-Pragmatic Pedagogical Approaches to English for Research Publication Purposes
Bibliographic record
Abstract
Despite the increasing demands on many multilingual scholars outside the centre(s) of scientific knowledge production to publish their research in international scholarly journals, the support for such academic writing for publication is uneven at best. Existing English for research publication purposes (ERPP) instruction typically aims to aid multilingual scholars in achieving genre-based expectations and/or navigating the submission and review process, but it often does not address the politics of English-language knowledge production. In this paper, informed by an empirical case study and a theory building perspective, we address the need for a sustained program of courses/workshops for multilingual scholars in the (semi-) periphery and propose a means of operationalizing a critical-pragmatic approach to such course/workshop content. Our empirically-driven model is informed by the results of a recent case study investigation into an intensive ERPP intervention designed to address multilingual Spanish-speaking L1 scholars’ challenges with writing research articles for publication in indexed (Web of Science) international scientific journals. Our model lays the groundwork for a more critical approach to ERPP pedagogy, one that attempts to attend more fully to the needs of multilingual scholars within an asymmetrical market of global knowledge production.
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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 | 0.071 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".