A bibliometric analysis of global research on drinking water and health in low- and lower-middle-income countries
Bibliographic record
Abstract
Heightened interest in drinking water research in recent decades has been aimed at narrowing the knowledge gaps surrounding water and health in a global pursuit to provide safely managed drinking water services to populations who continue to lack access. This study used bibliometrics and network analysis to produce a global overview of publications and groups that have contributed to research on drinking water and health in low- and lower-middle-income countries (LLMICs). The United States and the United Kingdom, which have historically dominated the field based on the production and impact of scientific literature, remain at the center of international collaborative research partnerships with emerging countries. However, in recent years, the volume of publications produced by India has surpassed that of the United States while Bangladesh is ranked third for the strongest international collaborations. Iran and Pakistan are also emerging as major producers of research, yet publications out of these countries and India remain disproportionately restricted behind paywalls. Contamination, diarrheal disease, and water resources are the themes that characterize the majority of research on water and health. These findings may be used to accelerate equitable, inclusive research in the realm of water and health, thereby enabling gaps in global drinking water inequalities to be filled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.034 | 0.021 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".