Emerging outcomes from a cross-disciplinary doctoral programme on water resource systems
Bibliographic record
Abstract
Interdisciplinary research and education programmes in water science are intended to produce groundbreaking research, often with an emphasis on societal relevance, and prepare future water resource experts to work across disciplines. This paper explores the emerging outcomes from an ongoing doctoral programme currently in its seventh year. Within the programme, there is both cross-disciplinary and mono-disciplinary research. Three questions are explored: (i) whether cross-disciplinary research leads to more innovative scientific findings than mono-disciplinary research, (ii) whether cross-disciplinary researchers develop professional skills that benefit their future careers, and (iii) whether cross-disciplinary research produces findings of greater societal relevance than mono-disciplinary research. Various indicators are used to measure research and education outcomes. Analysis of journal impact factors and citation rates of Institute of Scientific Information indexed publications suggests that cross-disciplinary research findings are more innovative. Comparison between graduate research profile and their career destinations suggests that researchers who learn to work across the disciplines continue to work this way in their post-doctoral positions. Analysis of media interest in research findings or their impact on policy suggests that both types of research are of societal value, but researchers often expand their understanding of a societal interest topic by bringing in new research fields.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".