Bibliographic record
Abstract
Abstract The chapter examines the main features and trends characterizing the European Union’s (EU) efforts in the digital diplomacy domain. The EU, like other national and international political entities, has recently embraced social media and other digital technologies as a way to engage with foreign audiences and raise its global profile. Because of its unique nature—a hybrid and unfinished political entity mixing intergovernmental and supranational features—the EU’s foray into digital diplomacy faces numerous challenges, from its communication strategy’s internal (i.e. within the EU) bias, to the lack of coordination among the various stakeholders involved, the competition with member states, to the ‘communication deficit’ that still besets the organization. As a ‘normative power’ with less historical baggage and a more positive reputation (at least outside Europe) than its member states, the EU has nonetheless the potential to be successful and effective in projecting its ‘soft power’ through digital channels. The regional organization has made some strides in this regard, but it has not fully exploited the opportunities that ‘going digital’ entails. The chapter elaborates on the challenges and opportunities in European Union digital diplomacy by providing empirical examples of EU efforts in this domain (the 2017 ‘European Way’ (EAAS 2017) social media campaign and the EU’s communication strategy during the Iran nuclear deal negotiations) and linking them to theoretical debates in the fields of international relations and communication.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".