Minorities in the Post-Soviet Space Thirty Years After the Dissolution of the USSR
Bibliographic record
Abstract
When the Soviet Union broke apart in 1991, the Russian Federation and the newly independent republics of the Baltics, the Caucasus and Central Asia engaged in redefining their national identity in a challenging regional and global context. The stances and policies towards the minorities living in these countries became part of the striving towards national independence and identity formation. Despite vastly different post-Soviet nation-building trajectories, the development and implementation of state policies towards minorities had similar relevance and importance across the region. Thirty years after the end of the USSR what is the situation of minorities and minority issues in the countries that emerged from that multi-ethnic state? How have the former republics – including Russia dealt with their minorities and minority affairs? To what protection and rights are minority communities entitled to? Studies of the dissolution of the USSR and of nation-building in the independent post-Soviet states have flourished over the past decades. However, despite the relevance of the theme, there is a dearth of specialist publications which address the many issues related to minority communities in the post-Soviet space. This volume attempts to fill this gap by providing a collection of essays covering some of the most relevant aspects of the contemporary status and situation of minorities in the area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".