The Globalization of Science: The Increasing Power of Individual Scientists
Bibliographic record
Abstract
National science systems have become embedded in global science and countries do everything they can to harness global knowledge to national economic needs. However, accessing and using the riches of global knowledge can occur only through scientists. Consequently, the research power of nations relies on the research power of individual scientists. Their capacity to collaborate internationally and to tap into the global networked science is key. The constantly evolving, bottom-up, autonomous, self-regulating, and self-focused nature of global science requires deeper understanding; and the best way to understand its dynamics is to understand what drives academic scientists in their work. We are particularly interested in the contrast between global science as a largely privately governed and normatively self-regulating institution and global science as a contributor to global collective public goods. The idea that science remains a state-driven rather than curiosity-driven is difficult to sustain. In empirical terms, we describe the globalization of science using selected publication, collaboration, and citation data from 2000-2020. The globalization of science implies two different processes in two different system types: the growth of science in the Western world is almost entirely attributable to internationally co-authored publications; its growth in the developing world, in contrast, is driven by both internationally co-authored and domestic publications. Global network science opens incredible opportunities to new arrivals—countries as well as institutions and research teams. The global system is embedded in the rules created by scientists themselves and maintained as a self-organizing system and nation-states have another major level to consider in their science policies: the global level. Globalization of science provides more agency, autonomy, collegiality, and self-regulation to scientists embedded in national science structures and involved in global networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.272 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".