Dividing United Europe. From Crisis to Fragmentation?
Bibliographic record
Abstract
Pictures of Angela Merkel in a Nazi uniform, the burning of German flags, newspaper articles portraying Southern Europe as work-shy and Northern Europe as tight-fisted: The Eurozone crisis has thrown up old stereotypes; often digging into well-established historical images of 'the other'. The conscious or tacit (ab)use of national prejudices by politicians and parts of the media, and the strong emotional reactions among European citizens have caused a lot of public concern about the likely negative implications of such reawakening of national cliches and the newly hardening boundaries they construct for the process of European integration. It is evident that current and recent crises confront European citizens with profound dilemmas which they seek to make sense of, and in response to which much new political mobilisation takes place. At the same time, some of the interpretative and political reactions thus generated also have the potential to become very destructive processes, putting into question years of integration efforts. This book brings together scholars who examine the nexus between (economic) crisis, national identities and the use of historical images, and prejudices and stereotypes, by focusing particularly on media and political discourses in different European countries. In addition to detailed empirical discussions covering diverse national settings across Europe, the different contributions discuss and offer a variety of conceptual and methodological approaches within the inter-disciplinary study of national identities, prejudice and stereotyping in the context of socio-economic and political crises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".