The countries and languages that dominate biological research at the beginning of the 21st century.
Bibliographic record
Abstract
Traditionally, studies of scientific productivity are biased in two ways: they are based on Current Contents, an index centered in British and American journals, and they seldom correct for population size, ignoring the relative effort that each society places in research. We studied national productivity for biology using a more representative index, the Biological Abstracts, and analyzed both total and relative productivity. English dominates biological publications with 87% (no other individual language reaches 2%). If the USA is considered a region by itself, it occupies the first place in per capita production of biology papers, with at least twice the productivity of either Asia or Europe. Canada, Oceania and Latin America occupy an intermediate position. The global output of scientific papers is dominated by Europe, USA. Japan, Canada, China and India. When corrected for population size, the countries with the greatest productivity of biology papers are the Nordic nations, Israel, Switzerland, Netherlands, Australia, Saint Lucia and Montserrat. The predominance of English as the language of biological research found in this study shows a continuation of the trend initiated around the year 1900. The large relative productivity of the USA reflects the importance that American society gives to science as the basis for technological and economic development, but the USA's share of total scientific output has decreased from 44% in 1983 to 34% in 2002, while there is a greater growth of science in India, Japan and Latin America, among others. The increasing share obtained by China and India may reflect a recent change in attitude towards funding science. The leadership of Nordic nations, Israel, Switzerland, Netherlands and Australia can be explained by cultural attitude. Apparently, a positive trend is emerging in Latin America, where Chile improved its ranking in per capita productivity but Argentina, Costa Rica, Uruguay, Brazil and Cuba fell. Nevertheless, the most productive countries in total number of papers are Brazil, Mexico and Argentina: large countries with a long tradition of funding scientific research.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".