Empire of Nations: Ethnographic Knowledge and the Making of the Soviet Union
Bibliographic record
Abstract
Francine Hirsch. Empire of Nations: Ethnographic Knowledge and the Making of the Soviet Union. Culture and Society after Socialism. Ithaca: Cornell University Press, 2005. xviii, 367 pp. 19 figures. 7 maps. Bibliography. Index. $59.95, cloth. $27.95, paper.In Empire of Nations, Francine Hirsch deals with fundamental questions pertaining to the formation of the Soviet Empire. The book's ambition is evident from the questions about the former Soviet Union asked on page one: Where had all these come from? What kind of state had the Soviet Union been? What was the Soviet socialist experiment all about? The questions have been asked before, but focusing on the cultural technologies of Soviet Hirsch provides important new insights. She traces the ways in which ethnographers and local elites provided the Bolsheviks with knowledge that was crucial to the very formation of the Soviet Union. They participated in producing ail-Union censuses, in delineating internal Soviet boundaries, and in designing ethnographic exhibits featuring the Soviet peoples. That is, they played crucial roles in the process which the Soviet Union became not only imagined, but immersed in the everyday life of its citizens. The book moves beyond critiques of the once pervasive myths of the Soviet Union as a consciously acting breaker of nations to actually documenting the haphazard and ambiguous politics-a work in progress-by which the Soviet Empire was created. In turn, these politics revealed fundamental ambiguities and tensions underlying Soviet ideology.The first two chapters outline these underlying tensions-between the need to create an integrated economy and commitment to national self-determination. Moreover, Hirsch shows that the Soviet concept of was ambiguous in itself, as it was premised both on the notion that nationalities were rooted in primordial ethnic groups and on the assumption that the state could intervene to construct modern nations. While these ambiguities explain many of the state's erratic decisions, the Soviet commitment to nationality also had the result that national categories became fundamental markers of identity, embedded not just in the administrative structure of the USSR, but also in people's consciousness. Indeed, the 1930s even rural and nomadic populations started to describe themselves as members of nationalities. To understand this accomplishment, Hirsch dissects several cultural technologies of rule: the census, the map, and the museum.Chapters three and four document how the contending views and interests of ethnographers, linguists, and state officials on various levels contributed to the creation, amalgamation, and repudiation of various ethno-national categories. In doing so, Hirsch not only manages to challenge the view of Soviet nationality policies as a top-down strategy to divide and rule, but also demonstrates that the map and the census were crucial in activating official categories by demonstrating that nationality, resources, and local political power were officially linked (p. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".