French Science Diplomacy and International Science: A Scientometric Analysis
Bibliographic record
Abstract
Scientific diplomacy involves the use of scientific and technical collaborations to foster international relations and to address global challenges. It promotes collaboration between nations through joint research and innovation projects, knowledge-sharing, and the application of scientific advancements to tackle shared issues and challenges in national and international contexts like climate change, public health crises, and technological innovation. France is a key player in international scientific organizations and agreements, emphasizing the importance of scientific and technological collaboration in addressing global issues. The primary source of data utilized in this scientometric study was the Web of Science citation database. The extracted data were recorded and analyzed using Excel software. This study shows the state of French scientific collaborations separately from each of the five continents. On the continent of America, France collaborated with 32 countries, with most scientific collaborations taking place with the United States, Canada, Brazil, Mexico, Chile, and Argentina. In Europe continent, France engaged in scientific collaborations with 49 countries, including Germany, the United Kingdom, Italy, Spain, Switzerland, Belgium, the Netherlands, etc. Similarly, in Asia continent, France fostered scientific relationships with 36 countries, consisting of China, Japan, India, and South Korea emerging as its most prominent partners. On the African continent, France also established scientific collaborations with 49 countries, especially with Tunisia, Algeria, Morocco, and South Africa. Finally, in the Oceania continent, France maintained scientific ties with 8 countries, especially with Australia, New Zealand, New Caledonia, Fiji, and Vanuatu. The current research shows that France has a rich tradition of engaging in science diplomacy and international collaboration, emphasizing on multilateralism and cultural influence.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.096 | 0.158 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".