Is it real? Science diplomacy in the Arctic states’ strategies
Bibliographic record
Abstract
Abstract Dominant geopolitical narratives on the Arctic argue that the region is either edging towards conflict or international law is respected and peace is maintained through cooperation to address shared concerns. While both of these narratives are present in the Arctic states’ strategies, most of them tend to support collaborative efforts. Science diplomacy (SD) is a useful mechanism in this regard as it helps states overcome potential disagreements and can stimulate cooperation in other areas. Given the growing concerns about global risks, SD is more important now than ever before. In our study, we identify and focus on three indicators for potential SD in the Arctic strategies: i) scientific infrastructure; ii) membership in intergovernmental/interparliamentary and scientific/education organisations and networks and iii) specific areas of scientific cooperation. Considering the intensive scientific activity in the region, it is not surprising that the strategies discuss different forms of research and scientific cooperation, although none explicitly use the term “science diplomacy”. Nevertheless, our analysis reveals the Arctic states do apply the SD concept in their strategies and use three types of diplomacy – science in diplomacy , science for diplomacy or diplomacy for science – in the three indicators.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".