Bibliographic record
Abstract
ABSTRACT In this article, I argue that Terézia Mora's 2004 novel Alle Tage foregrounds the Yugoslav wars, and the Kosovo intervention in particular, as significant events for processes of the Berlin Republic's political self‐fashioning in the late 1990s. I therefore contend that despite the text's more widely acknowledged global and delocalised aspects, Alle Tage is in fact a very local and ‘German’ text, directly engaging with the socio‐political contexts of the Berlin Republic. I start by addressing the ways in which the 1990s Balkan wars have been used to reposition questions of German identity in relation to its World War II past. I then examine and offer an alternative to the notion that Térezia Mora's novel Alle Tage is predominantly a global text by highlighting the text's inextricable embeddedness in discourses surrounding German identity in the 1990s. I do so by tracing the novel's simultaneous critiques of both the notion of a global, nomadic way of being as well as of essentialist conceptions of community such as a nation or ethnic belonging. As the fate of the novel's main character Abel illustrates, for Mora belonging is instead a matter of an embodied, experiential access to both one's past and present. I conclude by arguing that Mora's novel suggests that just like Abel, Germany cannot move beyond post‐war themes such as nationalism, war, and genocide without thereby committing violent acts of forgetting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".