The Growing Concentration of National Influence in Global Science and Its Impact on Future Research.
Bibliographic record
Abstract
A small group of prominent countries is disproportionately and increasingly influencing scientific discourses. Concentrated influence in science could stifle sustained innovation and exclude researchers in countries often relegated to the periphery of global science. To demonstrate the imbalance of national influence, I focus on a field’s concepts and ideas that originate from researchers in one country and are then used by researchers in other countries. Text is the best medium where these scientific concepts and ideas reside as terms that can be extracted at scale. I construct yearly international networks of term-based knowledge diffusion between the years 1990 and 2012 for 165 academic fields based on nearly a quarter million sets of scientific terms. I use 30 million scientific papers published from two metadata repositories of scientific publications, OpenAlex and Semantic Scholar Academic Graph. I measure misalignments between these diffusion networks and their corresponding citation networks that reflect the recognition of international influence. I find that both are consistently and strongly aligned. However, it is where knowledge originally diffuses from that reveals a growing and concentrated imbalance in national influence, in particular from the United States.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".