THE MAKING OF CRIMINAL LAW IN RUSSIA AND THE WEST: THE POLICY PROCESS, ADMINISTRATION, AND THE ROLE OF EXPERTS
Bibliographic record
Abstract
In the new millennium, in Russia and the West alike, criminologists regularly complain about a diminishing role for experts in the making and administration of criminal policy – in the West, because of pandering to the public (penal populism), and in Russia, a failure to take a systematic approach to crime control. In both places, this paper argues, these appraisals are based on idealized and unrealistic images of the way criminal law developed in the past. In North America, criminal policy-making has never conformed to a rational model as favored by some specialists in public administration. In Russia, the European ideal of a major role for criminal law scholars has been confined to periods of codification and has not served as the norm most of the time. In both parts of the world, it is essential that scholars study how criminal policy develops, in order to understand the current situation and find ways to contribute to its making.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".