Trends in global research on sanitation: a 30-year perspective from 1990 to 2019
Bibliographic record
Abstract
In the past three decades, the field of sanitation has attracted much academic attention, and a large volume of relevant research results have emerged. To explore the characteristics and trends of global sanitation research literature, this paper presents a bibliometric analysis of 9559 articles published between 1990–2019 and extracted from the Web of Science Core Collection database. With the rapid increase in the number of related publications, both the breadth and depth of sanitation studies have increased significantly in the 21st century. In terms of multiple criteria, the comprehensive research strength of developed countries is greater than that of developing countries. The field is highly interdisciplinary, meaning that collaborations between research institutions are increasing. Keyword clustering analysis shows that the main research topics in the domain of sanitation are: (i) drinking water, (ii) sustainability, (iii) biofilm, (iv) epidemiology, and (v) WaSH (water, sanitation and hygiene). Meanwhile, keyword burst analysis showed that the new research hotspots and frontiers mainly concentrated on: (i) sustainable development of sanitation services adapting to climate change, (ii) main determinants affecting child malnutrition, (iii) research based on global and multi-national data, and (iv) evaluations on various aspects of performance. This paper provides a better understanding of sanitation research trends that have emerged over the past 30 years and can serve as a reference for future research.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.059 | 0.112 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".