Silos to synergy: Experiences of an interdisciplinary trainee network
Bibliographic record
Abstract
With global increases in the population of older adults, there is a critical need for interdisciplinary collaboration to address the complexities of aging. Interdisciplinary research is well suited to facilitate trainee development; this venue provides opportunities for engagement in collaborative projects and networks, positioning trainees to become effective interdisciplinary researchers. This project examined the experiences of graduate students and post-doctoral fellows participating in an interdisciplinary trainee network for research on aging. The methods were informed by principles of reflexivity whereby participants reflected on their experiences of engaging in the network. Key findings included: the contribution of institutions and structures, transcendence of boundaries, and development at the level of the individual and community. Findings highlight the value of investing in trainee development in interdisciplinary collaboration, within and beyond aging research, and can inform the development of interdisciplinary trainee initiatives in other areas of research, policy, and practice.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.003 | 0.008 |
| 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".