Ready to Manage a Global Pandemic? Explaining the Involvement of the EU in the 2013–2016 Ebola Outbreak
Bibliographic record
Abstract
Abstract A virulent outbreak of Ebola Virus Disease killed thousands of individuals between December 2013 and June 2016. The risk of contagion among European Union (EU) citizens increased its salience to unprecedented levels for an outbreak that primarily affected sub-Saharan Africa. Considering the need for analyzing recent external transboundary outbreak responses in the post-COVID-19 era, this paper explains the involvement of the EU in the Ebola outbreak. By combining descriptive social network analysis with fourteen semi-structured interviews, it provides original insights into European politics and crisis management scholarship. The findings partially support theoretical expectations regarding the relevance of postcolonial ties and institutional frameworks in the reaction. It also suggests that neorealist literature fails to capture its full complexity. Hence, institutional deficiencies explain the low centrality and flawed coordination among EU actors in the response. Additionally, postcolonial ties with the affected countries facilitated the involvement of Western governments in the reaction. However, not all former colonial powers were equally involved in the response. Finally, countries that registered infections did not necessarily play central roles in this effort. These findings have broader implications for the involvement of the EU in future external outbreaks, including the need for establishing clearer and explicit allocations of competences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".