Bibliographic record
Abstract
This special issue brings together scholars who concentrate on the way ‘lessons from the past’ have been framed in different national contexts in Central and Eastern Europe. In the wake of crisis in Europe, bits and pieces of the past are being resurrected as a means of understanding the present and imagining the future. References to historical experiences and lessons from the past have started to reappear in public and private discourse. Most allusions go back to the first half of last century and the experience of war, oppression and dictatorship that have marked the beginning of European cooperation within a fixed institutional framework. ‘Lessons from the past’ have always played a considerable role in EU integration history. But how has their meaning changed over time? Which role do references to WWII, Nazism and Fascism, Civil War and the Holocaust still play in today’s debates on further integration? How is their relevance to the present disputed? What is the process through which they are revived and reanimated in contemporary debates? By gathering scholars from different universities in the Netherlands, Denmark, Belgium, Poland, Germany and the US with different disciplinary backgrounds (political science, anthropology, history, sociology, law, cultural studies) and from different generations, this special issue examines the topic from the widest possible angle. In addition to detailed empirical discussions covering diverse national settings across Central and Eastern Europe, the different contributions and the introduction by the guest editor discuss and offer a variety of conceptual and methodological approaches within the inter-disciplinary study of memory and identity in the context of socio-economic and political crises.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".