Bibliographic record
Abstract
This article aims to bridge the gap in our knowledge about Iranian prisons and the sociodynamic relations that animate them by illuminating the characteristics and activities of prison gangs in Iran. The interaction between gang affiliation and drug networks, security and violence will be discussed in detail. The in-depth qualitative research, which is informed by grounded theory, serves as the first academic study of gangs in Iranian prisons. Research participants included 38 males and 52 females aged 10–65 years. They were recruited in several different settings, both governmental and non-governmental organizations. The study employed theoretical sampling and in-depth, semi-structured interviewing. Results show that gang-affiliated inmates in Iranian prisons gain monopoly over the drugs market inside prison networks, which leads to inevitable extortion of both prisoners and correctional officers. Gang affiliation blurs the lines between violence and safety, while providing a sense of identity, belonging and financial and emotional support. Prison gang membership also offers some benefits to prisoners and staff, as their existence underpins an informal social order that can be used to govern prisoners. The article discusses this less well-known and unexplored dimension of the topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".