Estimates of global research productivity in virology
Bibliographic record
Abstract
The quantity and quality of published research in the field of Virology by different world regions was estimated in this study. Using the PubMed database, articles from journals included in the "Virology" category of the "Journal Citation Reports" database of the Institute for Scientific Information for the period 1995-2003 were retrieved. The world was divided into nine regions based on geographic, economic, and scientific criteria. Data on the country of origin of the research was available for 33,425 out of 33,712 articles (99.2% of all articles from the included journals). USA exceeds all other world regions in research production for the period studied (42% of total articles), with Western Europe ranking second (35.7%). The mean impact factor in articles published in Virology journals was highest for the USA (4.60), while it was 3.90 for Western Europe and 3.22 for the rest of the world (seven regions combined). USA and Canada ranked first in research productivity when both gross national income per capita (GNIPC) and population were taken into account. The results of this analysis show a distressing fact; the absolute and relative production of research in the field of Virology by the developing regions is very low, although viral diseases cause considerable morbidity and mortality in these areas. It is evident from this study that developing regions need more help from the developed regions to enhance research infrastructure.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.052 | 0.066 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".