A bibliometric analysis of drinking water research in Africa
Bibliographic record
Abstract
A total of 1 917 publications of drinking water research in Africa from 1991 to 2013 were identified from the data hosted in online version of SCI-Expanded, Thomson Reuters Web of Science, for bibliometric analysis. The analysis included publication output, distribution of keywords, journals and subject areas, and performances of countries, institutions, and authors. Citation trends and highly-cited publications are also reported. We found that the publication output of related documents increased over the entire period of study. The results showed that ‘water’, ‘drinking water’, and ‘oxidative stress’ were the most frequent terms in publication titles, authors’ keywords and KeyWords Plus. The top three subject areas were ‘water resources’, ‘environmental science’, and ‘environmental and occupational public health’. The ten most productive institutions were located in South Africa and Egypt, and the University of Pretoria was the overall most productive institution. Thus, a quarter of all of the articles published were from South Africa. It was found that articles became increasingly collaborative with greater numbers of authors, page counts and bibliographies. More than half of the internationally collaborative articles were co-authored with researchers from Europe. French and US institutions contributed to the highest number of collaborative articles.Keywords: Africa, bibliometric review, drinking water, publications, research collaborations, water 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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.175 | 0.260 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".