Bibliographic record
Abstract
Since the collapse of the Soviet multinational state-empire, nationalism in post-Soviet states has been the subject of an ever-increasing number of studies. Post-Soviet scholars have adopted a wide variety of studies on different aspects of the relationship between nation- and state-building projects. In the midst of this burgeoning interest in post-Soviet nationalism, however, there has been relatively little interest in the Soviet era itself. Recent studies provide a valuable re-assessment of some aspects of the ‘national question’ in the Soviet Union. However, the scope of these studies has been relatively limited, and primarily undertaken as historical research. An example can be found in the works of Ronald Suny and Terry Martin. These two authors have re-opened evaluations of the early Soviet period and the ‘national question’. However, they focus mostly on the Leninist and Stalinist periods of Soviet history (Martin, 2001; Suny and Kennedy, 1999). Most recent studies of the Soviet Union generally do not study the entire Soviet project; and even if they do, very few have attempted to link it to the post-Soviet projects taking place today. With the exception of Ronald Suny and Rogers Brubaker, (Brubaker, 1996; Suny, 1999), there has been relatively little theoretical discussion of the Soviet nationalities model and its importance for understanding nationalism.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".