“I am working 24/7, but I can't translate that to you”: The barriers, strategies, and needed supports reported by chemistry trainees from English-as-additional language backgrounds
Bibliographic record
Abstract
Having a shared language such as English facilitates globalization of science but it can also limit science by poses barriers for those who have learned English-as-an-additional language (Eng+). There is currently no research focused on the experi-ences of Eng+ graduate students and postdoctoral researchers (“trainees”) in chemistry and related sciences, despite the grow-ing prominence of Eng+ trainees and ongoing calls for improving equity and inclusion in the sciences. Without research focused on Eng+ trainees, we risk losing out on a diversity of perspectives and expertise that might otherwise strengthen scientific progress. This study represents a first step in developing and evaluating strategies that effectively support Eng+ chemistry trainees by investigating their experiences with learning, communicating, and doing chemistry in English. This research was guided by three research questions: (1) What are Eng+ chemistry graduate students’ and post-doctoral research-ers’ (i.e., trainees) language experiences before entering English-dominant research programs? (2) What language barriers have trainees faced in learning, communicating, and doing chemistry? and (3) What strategies and supports have been helpful or needed? To investigate these RQs, we conducted semi-structured focus groups and interviews with 18 Eng+ chemistry graduate students and post-doctoral researchers, followed by qualitative analysis to identify key themes. We found that trainees had diverse language experiences before entering English-dominant research programs and faced challenges working in an English research environment. Trainees described research impacts that included avoiding interactions with colleagues, exacerbated feelings of imposter syndrome, needing extra time to learn or communicate, and not being able to fully express their knowledge. They expressed an ongoing desire to learn English and described their various independent strategies, demonstrating resilience and growth mindset to navigate challenges. All participants believed that research supervisors and institutions needed to play a larger role in supporting Eng+ trainees, such as empathetic supervisors who offer personal and professional support, and institutional supports that provide chemistry-specific knowledge-building and networking opportunities. Based on the findings, we recommend approaches that supervisors and institutions can enact that may improve linguistic equity in the chemical sciences.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".