Research Career Intentions Among Non-Native English-Speaking Graduate and Postdoctoral Trainees in STEM—Results from Cross-Sectional and Longitudinal Studies
Bibliographic record
Abstract
International graduate trainees, many of whom are non-native English-speaking (L2) trainees, comprise more than half of graduate-level trainees in STEM, but little is known regarding factors that influence their career intentions, especially those that foster their growth as scientists to achieve their full potential in research. Thus, the purpose of our studies was to examine the relationship between L2 status and contextual factors that help shape the learning experiences and plans for research-focused careers. Study 1 collected cross-sectional survey data from doctoral and postdoctoral trainees (N=510) from research institutes in the Texas Medical Center in Houston. We examined which factors were associated with research career intentions using multiple linear regression analysis. Study 2 collected longitudinal data from doctoral and postdoctoral trainees (N=185) from 71 institutions in 33 states in the U.S. Repeated measures of career intentions were evaluated using mixed-effect modeling, and cross-tabulation analysis evaluated job-seeking behaviors by language status. Results showed that L2 trainees had stronger intentions to pursue research careers than did native English-speaking trainees (L1), controlling for other variables. Mentoring, trainee self-efficacy, and the perception of working more than mentors expected influenced each career intention differently. In Study 2, career intentions did not change over time for L2 or L1 trainees, but L2s preferred working in higher education or research institutes more than L1s. L2s, however, were more likely to be in early stages of seeking jobs compared to L1s. These findings provide implications for research mentors, advisors, and academic administrators in facilitating L2 career advancement and success.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".