The COVID-19 pandemic and the European Union: politics, policies and institutions
Bibliographic record
Abstract
The COVID-19 pandemic posed unprecedented challenges to the European Union (EU) and its member states. In the EU, health policy competence has been and remains largely with member states. However, faced with a major external crisis, which more or less affected all member states at the same time, the EU developed a framework within which the member states (and their subnational units) could respond together to the crisis. This introductory article to the Special Issue ‘The COVID-19 Pandemic and the European Union,’ briefly examines how EU institutions, policies and politics were affected by the crisis. Contrary to earlier crises, the EU responded speedily and effectively this time around. The EU has become increasingly important in crisis management, in part due to the nature of transboundary crises. The EU proved itself to be a good crisis manager on some dimensions, but certainly not on all. The crisis created momentum for collective action and for fast decision-making, even though the legitimacy of some these actions has been subject to limited public scrutiny.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.014 | 0.005 |
| 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".