Promoting community and competence: the development and evaluation of an international research training network of sexual and gender diverse (SGD) emerging scholars
Bibliographic record
Abstract
Specialized research training is a key component of graduate education, yet sexual and gender diverse (SGD) emerging scholars may not receive quality training and networking opportunities at their home institutions. International and interdisciplinary trainings by SGD scholars may develop research competence and academic networks, but few such extracurricular research training programs exist. This article presents the curriculum and mixed-method evaluation of the International Student Training Network (ISTN), a two-year bilingual training program designed to train SGD emerging scholars in Canada, the USA, Mexico, and the UK to conduct research with SGD youth. The racially diverse and interdisciplinary trainees (N = 38) completed a competence self-assessment at pre-test, midpoint, and post-test. Significant improvements in knowledge and skill were found, while importance of the concepts remained consistently high. Twelve trainees participated in interviews to reflect on their experience. Thematic analysis produced three themes, describing benefits of the ISTN: (1) ‘You do stick out a lot’: Fostering SGD scholarly community in academia; (2) ‘We were all working together’: Bridging the disciplinary and geographic gaps; and (3) ‘A transformative experience’: Developing scholarly self-concept and academic self-efficacy. The findings highlight the utility of specialized research training for emerging SGD scholars limited by geographical and disciplinary siloes.
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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 | 0.039 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".