Common Interests Without Common Expertise: Reflections on Early-career Experiences in Cross-Disciplinary Research
Bibliographic record
Abstract
Cross-disciplinary research enables us to tackle complex problems that require expertise from different fields. Such collaborations involve researchers who have different perspectives, communication styles, and knowledge bases, and can produce results far greater than the sum of their parts. However, in an era of increasing scientific specialization, there exist many barriers for students and early-career researchers (ECRs) interested in training and undertaking interdisciplinary research endeavors. This perspective examines the challenges that students and ECRs perceive and experience in cross-disciplinary work and proposes pathways to create more inclusive and welcoming research environments. This work emerges from a National Science Foundation (NSF)-funded workshop held during the Society for Integrative and Comparative Biology (SICB) Annual Meeting in January 2023 in Austin, TX. The workshop brought together seasoned interdisciplinary scientists with undergraduate and graduate students to identify and discuss perceived challenges through small group discussions and experience sharing. Through summarizing a range of student concerns about embarking on careers as interdisciplinary scientists and identifying ways to dismantle institutional and lab management-level barriers, we aim to promote an inclusive and collaborative problem-solving environment for scientists of all experience levels.
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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 | 0.050 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.047 | 0.038 |
| Scholarly communication | 0.029 | 0.019 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.011 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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".