Hydrology Research Articles Are Becoming More Interdisciplinary
Bibliographic record
Abstract
We used Natural Language Processing (NLP) to assess topic diversity in the abstracts of all research articles (75,000) from eighteen water science and hydrology journals published between 1991 and 2019 -- these are all water science journals with an SCI h-index > 0.9. We found that individual water science and hydrology research articles are becoming increasingly interdisciplinary in the sense that, on average, the number of sub-topics that are represented in individual articles is increasing. This is true even though the body of water science and hydrology literature as a whole is not becoming more topically diverse. These findings suggest that the National Research Council's (1991) recommendation to increase multidisciplinarity of hydrological research has been followed in the sense that individual researchers are working to make their work more interdisciplinary. Topics with the largest increases in popularity were ‘Forecasting’ and ‘Climate Change Impacts’, and topics with the largest decreases in popularity were ‘Hydraulics’, ‘Solute Transport’, and ‘Aquifers and Abstraction’. Out of the eighteen journals that we tested, Hydrological Processes, Journal of Hydrology, and Water Resources Research are the three most topically diverse journals in the discipline. We also identified topics that are becoming increasingly isolated, and could potentially benefit from integrating more with the wider hydrology discipline.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.060 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".