At the Intersection of Modernities: Migrants as Agents of Economic and Cultural Change
Bibliographic record
Abstract
This article analyzes the infusion of North American ideas, culture and experience into the Soviet society, and depicts immigrants as agents of social and cultural change. Having embodied North American representations of modernity, they introduced new working methods, values and routines, as well as novel forms of labor organization to Soviet Karelia. With the help of imported machinery, tools, and equipment, immigrants drastically increased the production rates at Karelia’s industries. Glorified and publicized by the Karelian government and the media, their labour shops, and farm communes became models to be emulated in Karelia and throughout the Soviet Union. North American Finns also came to play an important part in Soviet elites’ attempt to modernize the Soviet society, both economically and culturally. Although they were agents of cultural and technological change in their own right, immigrants’ social and ethnic identities were subsumed and appropriated by the Soviet state and Karelia’s cultural producers in attempts to promote a Soviet version (an alternative) of economic and cultural modernity. Their physical and intellectual movement across national borders shaped and reconstituted the Soviet path to modernization, even if for a short period of time. In the process, concepts of the local, the national and the modern (that is, the global), became increasingly entangled.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".