Addressing the “Bias Gap”: A Research-Driven Argument for Critical Support of Plurilingual Scientists’ Research Writing
Bibliographic record
Abstract
This article outlines findings from a case study investigating attitudes toward English as the dominant language of scientific research writing. Survey and interview data were collected from 55 Latin American health and life scientists and 7 North American scientific journal editors connected to an intensive scholarly writing for publication course. Study findings point to competing perceptions (scientists vs. editors) of fairness in the adjudication of Latin American scientists’ research at international scientific journals. Adopting a critical, plurilingual lens, I argue that these findings demand a space for more equity-driven pedagogies, policies, and reflective practices aimed at supporting the robust participation of plurilingual scientists who use English as an additional language (EAL). In particular, if equity is indeed a shared goal, there is a clear need for commitment to ongoing critical self-reflection on the part of scientific journal gatekeepers and research writing support specialists.
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 | 0.378 | 0.459 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.022 | 0.149 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".