Bibliographic record
Abstract
Colloquially, the wars that led to the dissolution of Yugoslavia (between 1991 and 1995 in the western part of the country and 1999 in Serbia) are seen as the result of an upsurge of atavistic ethnic hatreds, which for decades slumbered below the fragile surface of the Yugoslav political and social order. However, more convincing is the argument that they had an entirely ‘European’ and even a modern and rational function of creating culturally and/or ethnically homogeneous nation-states instead of sustaining the traditional coexistence, communication, mixing and symbiosis of various groups with rather ambiguous and unstable ‘identities’. The function of these wars was to separate the communities by various kinds of ‘ethnic cleansing’, to draw territorial borders between them, and to solidify their particular ‘national identities’. 1 This argument is all the more convincing as in Europe in general and in the European Union (EU) in particular, nationalism, although in rather domesticated forms, is still stronger than the feeling of belonging to a common European political formation and the conviction of possessing a citoyenneté européenne — European citizenship. 2 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".