Interdisciplinary trainee networks to promote research on aging: Facilitators, barriers, and next steps
Bibliographic record
Abstract
Interdisciplinary education and research foster cross disciplinary collaboration. The study of age and aging is complex and needs to be carried out by scholars from myriad disciplines, making interdisciplinary collaboration paramount. Non-formal, extracurricular, and interdisciplinary networks are increasingly filling gaps in academia's largely siloed disciplinary training. This study examines the experiences of trainees (undergraduate, graduate, and post-graduate students) who belonged to one such network devoted to interdisciplinary approaches to education and research on aging. Fifty-three trainees completed the survey. Among respondents, some faculties (e.g., Health Sciences) were disproportionately represented over others (e.g., Business, Engineering, and Humanities). Most trainees valued their participation in the interdisciplinary network for research on aging. They also valued expanding their social and professional network, the nature of which was qualitatively described in open-text responses. We then relate our findings to three types of social capital: bonding; bridging; and linking. Finally, we conclude with recommendations for the intentional design and/or refinement of similar networks to maximize value to trainees, provide the skills necessary for interdisciplinary collaboration, and foster egalitarian and representative participation therein.
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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 | 0.041 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".