THE PRISON JOURNAL / June 2001Lankenau / CIGARETTES IN U.S. PRISONS SMOKE ’EM IF YOU GOT ’EM: CIGARETTE BLACK MARKETS IN U.S. PRISONS AND JAILS
Bibliographic record
Abstract
Since the mid-1980s, cigarette-smoking policies have become increasingly restrictive in jails and prisons across the United States. Cigarette black markets of various form and scale often emerge in jails and prisons where tobacco is prohibited or banned. Case studies of 16 jails and prisons were undertaken to understand the effects of ciga-rette bans versus restrictions on inmate culture and prison economies. This study describes how bans can transform largely benign cigarette “gray markets, ” where cigarettes are used as a currency, into more problematic black markets, where ciga-rettes are a highly priced commodity. Analysis points to several structural factors that affected the development of cigarette black markets in the visited facilities: the archi-tectural design, inmate movement inside and outside, officer involvement in smug-gling cigarettes to inmates, and officer vigilance in enforcing the smoking policy. Although these factors affect the influx of other types of contraband into correctional facilities, such as illegal drugs, this study argues that the demand and availability of cigarettes creates a unique kind of black market. Since the mid-1980s, cigarette-smoking policies have become increas-ingly restrictive in jails and prisons across the United States. Currently, two thirds of U.S. jails and one quarter of U.S. prisons ban inmates from smoking cigarettes or possessing tobacco (Falkin, Strauss, & Lankenau, 1998, 1999). In institutions where bans are enforced, inmates are prohibited from smoking
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.001 |
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".