National Stereotyping, Identity Politics, European Crises (Volume 27)
Bibliographic record
Abstract
The articulation of collective identity by means of a stereotyped repertoire of exclusionary characterizations of Self and Other is one of the longest-standing literary traditions in Europe and as such has become part of a global modernity. Recently, this discourse of Othering and national stereotyping has gained fresh political virulence as a result of the rise of “Identity Politics”. What is more, this newly politicized self/other discourse has affected Europe itself as that continent has been weathering a series of economic and political crises in recent years. The present volume traces the conjunction between cultural and literary traditions and contemporary ideologies during the crisis of European multilateralism. Contributors: Aelita Ambrulevičiūtė, Jürgen Barkhoff, Stefan Berger, Zrinka Blažević, Daniel Carey, Ana María Fraile, Wulf Kansteiner, Joep Leerssen, Hercules Millas, Zenonas Norkus, Aidan O’Malley, Raúl Sánchez Prieto, Karel Šima, Luc Van Doorslaer,Ruth Wodak
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.478 | 0.362 |
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; both teacher heads agree on what is shown here.
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".