Science diplomacy in the European Union: mapping the Portuguese case (1986–2021)
Bibliographic record
Abstract
Science diplomacy has been assuming a growing importance in the actions of states due to their need to respond to global challenges and to strengthen their power and influence through competitive advantage based on science and technology. Within the European Union (EU), science diplomacy can be seen as an instrument for the integration of the European project and for the projection of EU influence, standards, and values in the relationship with third countries. In this context, the present work aims at understanding and characterizing Portugal’s science diplomacy model and its relationship with the European project from 1986 (the moment of Portugal’s accession to the European Economic Community) to 2021. To this end, we mapped the science diplomacy designed and implemented by the Portuguese State by identifying its different instruments based on a methodology suggested by the European Commission, Directorate-General for Research and Innovation and Van Langenhove in 2017 and using a timeframe suggested by Heitor in 2015. The obtained dataset was subjected to a combination of analytical frameworks, including the general framing analysis proposed by Ruffini and Krasnyak in 2023, which allowed us to identify the objectives, strategic drivers and implementation approach of the Portuguese science diplomacy model. In the period under study Portugal has been developing a science diplomacy in parallel with the European project without ever losing a global vision of the relations in the field of science and technology.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".