Research contribution of different world regions in the top 50 biomedical journals (1995–2002)
Bibliographic record
Abstract
We evaluated all articles published by different world regions in the top 50 biomedical journals in the database of the Journal Citation Reports-Institute for Scientific Information for the period between 1995 and 2002. The world was divided into 9 regions [United States of America (the U.S.), Western Europe, Japan, Canada, Asia, Oceania, Latin America, and the Caribbean, Eastern Europe, and Africa] based on a combination of geographic, economic and scientific criteria. The number of articles published by each region, the mean impact factor, and the product of the above two parameters were our main indicators. The above numbers were also adjusted for population size, gross national income per capita of each region, and other factors. Articles published from the U.S. made up about two-thirds of all scientific papers published in the top 50 biomedical journals between 1995 and 2002. Western Europe contributed approximately a quarter of the published papers while the remaining one-tenth of articles came from the rest of the world. Canada, however, ranked second when number of articles was adjusted for population size. The U.S. is by far the highest-ranking country/region in publications in the top 50 biomedical journals even after adjusting for population size, gross national product, and other factors. Canada and Western Europe share the second place while the rest of the world is far behind.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.066 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| 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".