Highly cited articles in malaria research: a bibliometric analysis
Bibliographic record
Abstract
Purpose The purpose of this paper is to reveal the bibliometric characteristics of highly cited articles in Malaria research for the period of 1991-2015. Design/methodology/approach The data of highly cited articles for the period of 1991 to 2015 were extracted from the Science Citation Index Expended of Web of Science. The keyword “Malaria” was used as topic term to search documents that contained this word in the title or keyword or abstract of the documents that published in 1991 to 2015. A total of 1,614 articles having TC2015= 100 were retrieved as highly cited articles for further analysis, and Microsoft excel was used for the analysis purpose. Findings A total of 1,614 of highly cited articles were published in the 230 journals for the period of 1991 to 2015, and majority of the articles were appeared in journals that have top impact factor. The articles published in the 2011s have greater average citations and authors per article. Six journals have produced almost a quarter of highly cited articles and remaining articles were published in 224 journals. Proceedings of the National Academy of Sciences of the USA was the most productive journal with 154 articles, which accounts for 9.53 per cent of the total articles, followed by Lancet (110; 6.81 per cent). We found degree collaboration value of 0.971 for the articles, which indicates the clear dominance of multiple authors in publication of highly cited articles in Malaria research. In this study, new indictor calledPindex was applied for the evaluation of the author’s productivity. As per thep-value, the White, NJ has emerged as the most productive author with thep-value of 0.41 (61 articles), followed by Marsh, K (p= 0.33), Nosten, F (p= 0.32) and Snow, RW (p= 0.31). The USA and the UK were the most productive countries. The article entitled as “Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data” contributed by Lopezet al.(2006) was the most cited article with 2,245 citations in 2015. Research limitations/implications The data for the present study was limited to the publications that indexed in Science Citation index Expended of Web of Science. Originality/value This paper would be useful to the researchers to know the trends and achievements in the Malaria research and also to the library and information science professionals in collection building process.
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 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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.086 | 0.096 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".