A Bibliometric Analysis on Viral Central Nervous System Infection Research Productivity in Southeast Asia
Bibliographic record
Abstract
Research productivity on viral infections of the nervous system in Southeast Asia (SEA) is unknown. We aimed to determine the research productivity of SEA in terms of bibliometric indices and PlumX metrics and their correlation with socioeconomic factors. A comprehensive search of major electronic databases was done to identify studies on viral infections of the nervous system with at least one author from SEA. Socioeconomic factors and collaborations outside SEA were determined. Correlational analysis was done on bibliometric indices and socioeconomic factors. A total of 542 articles were analyzed. The majority came from Thailand (n = 164, 30.2%). Most articles used a descriptive study design (n = 175, 32.2%). The most common topic was Japanese encephalitis (n = 170, 31.3%). The % gross domestic product allotted for research, number of neurologists, and number of collaborations outside SEA correlated with the bibliometric indices and PlumX metrics. In conclusion, the number of research from SEA was low but the quality was comparable to the global benchmark. Improving resource allocation and collaboration between SEA nations and other countries may support this endeavor.
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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.110 | 0.128 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".