Supporting early career researchers: insights from interdisciplinary marine scientists
Bibliographic record
Abstract
Abstract The immense challenges associated with realizing ocean and coastal sustainability require highly skilled interdisciplinary marine scientists. However, the barriers experienced by early career researchers (ECRs) seeking to address these challenges, and the support required to overcome those barriers, are not well understood. This study examines the perspectives of ECRs on opportunities to build interdisciplinary research capacity in marine science. We engaged 23 current and former graduate students and postdoctoral fellows in a policy Delphi method with three rounds of surveying that included semi-structured questionnaires and q-methodology. We identified the following five barriers that limit ECRs’ capacity for interdisciplinary research: (i) demanding workloads; (ii) stress linked to funding, publishing, and employment uncertainty; (iii) limited support for balancing personal and professional commitments; (iv) ineffective supervisory support; and (v) the steep learning curve associated with interdisciplinary research. Our analysis highlights three main types of responses to these barriers adopted by ECRs, including “taking on too much”, “coping effectively”, and “maintaining material wellbeing at any cost”. To overcome these barriers, we propose the following three institutional actions to build early career interdisciplinary researcher capacity: formalize mentorship, create interdisciplinary research groups, and mainstream mental health support.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".