The Social Life of Illegal Drug Users in Prison: A Comparative Perspective
Bibliographic record
Abstract
Different degrees of criminalization of illegal drug use influence the overall composition of prison populations across countries. Individuals convicted of illegal drug-related crimes represent a significant part of the prison population in Kazakhstan, Russia and Ukraine. These convicts are not a part of the traditional criminal milieu, but a product of the perception of certain acts as crimes in particular contexts (e.g., an increasing social distance, the state’s control over potentially dangerous groups, etc.). The experience of incarceration might cause important changes in their life after release. The idea that prison contributes to the interiorization of criminal norms rather than preventing deviant behavior in the future seems especially fruitful in regard to illegal drug users. Elements of prison subculture are described on the basis of an empirical research conducted in 1996-2003 in Russian prisons (N = 769 (49 of these were convicted in relation to illegal drugs) in 2000-2001; and N = 214 (24) in 2003). The social organization of everyday life of inmates convicted of drug-related offences in Russia, Kazakhstan (N = 396 (76) in 2001) and Ukraine (N = 208 (26) in 2003) is compared with that of other convicts to test the hypothesis about the lack of significant differences between the two groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".