English Medium Publications: Opening or Closing Doors to Authors with Non-English Language Backgrounds
Bibliographic record
Abstract
This critical exploratory study aims to examine the role academic brokers play in opening (or not) the gates to non-first-language-English (NFLE) scholars to contribute to the global research conversation. For the study, a qualitative research approach was used to collect data; ten emergent and established researchers were interviewed, all of whom originated from non-Anglophone countries. Four academic brokers were also interviewed to further examine the topic from their viewpoints. The findings revealed that revisions recommended by journal editors and reviewers could perhaps diminish the richness of texts and ultimately affect the voices NFLE authors try to project in their papers. Findings also showed that academic brokers are cognizant of the problems NFLE authors face when writing for publication, especially those pertaining to the quality of their writing and to the ways they respond to reviewers’ suggestions and handle the review process.
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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 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".