New Soviet Gypsies: Nationality, performance, and selfhood in the early Soviet Union by Brigid O'Keeffe (review)
Bibliographic record
Abstract
The concept of a Romani ‘nation’ still triggers controversy among scholars and policy-makers alike. Several decades before the emergence of the Romani political movement as we know it today, the idea of Romani nationhood was debated, perhaps for the very first time, in the early Soviet Union. In theory, the Soviet state allowed all non-Russians to practise their own nationhood. This included the promotion of their language, schools, theatres, and other institutions. But the Roma had been considered since imperial times to be exotic and backward, properties that were not the typical features that would define a national minority. The Roma therefore constituted an interesting test case for the policy - both in regard to what defines ‘nationality’ and in regard to the opportunities for ‘advancement’ that the Soviet state purported to offer to minority groups (p. 6). Brigid O'Keeffe tells the fascinating story of the makings of Romani nationhood in the early years of the Soviet empire. Her thesis is that Soviet nationalities policy offered Roma an opportunity to perform their belonging to the state by displaying particularity. Within the broader context of early Soviet nationalities policy, a display of selfhood was advantageous. The public discussion of minority nationhood gave Roma a chance to define both what is Roma and what is Soviet.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".