Inmate Society in the Era of Mass Incarceration
Bibliographic record
Abstract
The origins and contours of inmate social organization were once central research areas that stalled just as incarceration rates dramatically climbed. In this review, we return to seminal works in this area and connect these with six interrelated changes to correctional contexts that accompanied mass incarceration. We argue that changes in prison racial, age, crowding, gender, offense type, and managerial characteristics potentially altered inmate informal organization and have yet to receive adequate criminological attention. We review the few recent studies that document contemporary inmate social life and call for increased researcher-practitioner partnerships that achieve mutual goals and embed criminologists within carceral settings. We suggest that network approaches are particularly useful for building on past qualitative and ethnographic insights to provide replicable results that are also easily conveyed to correctional authorities. As the era of mass incarceration peaks, we assert that the time is ripe for renewed interest in inmate society and its connections to prison stability, rehabilitation, and community reintegration.
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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 | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".