Bibliographic record
Abstract
Science diplomacy is a fast-growing field of research, education and practice dedicated to better understanding and reinforcing the connections between science, technology and international affairs, in order to tackle national and global challenges. Interest from early-career scientists and young diplomats to learn more and engage at the science-diplomacy nexus is growing all around the world. However, as a relatively new and multidisciplinary field, we show that science diplomacy has so far been largely taught through extracurricular courses and workshops, often self-organized by university student groups or international scientific organizations, targeting specific disciplinary and geographic audiences. Given this fragmented landscape, we map and categorize current science diplomacy educational offerings in higher education. Despite some coverage of science diplomacy within general science policy programs or courses focused on an issue area (e.g. water diplomacy or environmental diplomacy), a structured foundational course addressing the commonalities of all the scientific and technological issues relevant to international affairs is still lacking. Hence, we first suggest knowledge and key skills scientists and diplomats can learn from each other to bridge the disciplinary divide and engage in science diplomacy scholarship and practice. Building upon it, we then propose cross-cutting, core concepts that can inform the establishment and consolidation of science diplomacy curricula at universities. These aim to be useful to teach science diplomacy to advanced undergraduate and graduate students of all backgrounds and to be adaptable to a wide range of degree programs and disciplines.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".