Imperial <i>Farbrekhers</i> : Jewish Men and Crime in Tsarist Russia and Progressive New York City
Bibliographic record
Abstract
Abstract Between 1791 and 1917, the Russian Empire was home to the world’s largest Jewish population, almost all of whom lived as imperial subjects within the Pale of Settlement. While scholars have drawn on early-century sociological and anthropological studies of the Pale region to effectively document the daily lives of the area’s five million Jews, literature on Jewish crime and punishment under imperial surveillance remains underdeveloped. This chapter begins by synthesising the available literature on Jewish encounters with Imperial police, courts and corrections before looking at the more than one million Jews who immigrated from Russia to New York City throughout the 1890s and 1900s. Employing data from Russian social scientists, along with records on Jewish-American crime from the American Jewish Committee and the United States Desertion Bureau, this chapter considers how Jews maintained, altered or abandoned criminal behaviours upon transitioning from subjects of the Russian Empire to immigrants in the United States. In comparing arrest and incarceration records in Progressive-era New York City and Tsarist Russia, this chapter reflects upon the circumstances that allowed Jews to maintain low rates of arrest and incarceration in both milieus. This chapter contends that a century of shtetl life and ghettoisation in Russia shaped Jewish criminality along material lines while minimising the functional need for violence, thereby priming Jewish immigrants for relative success within industrialised North American cities that vilified casual violence and accepted those forms of economic crime common among Jews as largely victimless and morally nebulous.
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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 | 0.000 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".