Crime and criminal justice after communism: Why study the post-Soviet region?
Bibliographic record
Abstract
This special issue focuses on crime and criminal justice in the former Soviet Union (FSU), a world region we believe should be better known to criminologists. Together with all our fellow authors, we hope to convey to readers the fascination of the postSoviet region and the thought-provoking criminological questions that it prompts. We also aim to stimulate debate about what criminologists elsewhere in the world can learn from the FSU, and to consider how criminology in the region itself might develop. In this introduction, we present three distinct theoretical contributions that research on postSoviet crime and criminal justice can make to the global scholarly community. First, we discuss the theoretical utility to criminologists of the post-Soviet transition. Transitions to democracy and the market, whether in South America, Africa, or the former communist bloc, are notable for rapid increases in crime. Yet, we suggest that the transition paradigm now holds diminishing value, after two decades of post-Soviet development in which post-Soviet states have diverged substantially in both incidence of crime and responses to it. Second, as a corollary, we argue that the collapse of the Soviet Union and the subsequent development of its successor states provide a natural experiment that helps us understand divergence in crime and criminal justice policy. The FSU provides rich opportunities for both intra-regional and inter-regional comparison. It thus has much to offer to the rapidly developing subfield of comparative criminology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".