Toward a Criminology of Inmate Networks
Bibliographic record
Abstract
The mid-twentieth century witnessed a surge of American prison ethnographies focused on inmate society and the social structures that guide inmate life. Ironically, this literature virtually froze in the 1980s just as the country entered a period of unprecedented prison expansion, and has only recently begun to thaw. In this manuscript, we develop a rationale for returning inmate society to the forefront of criminological inquiry, and suggest that network science provides an ideal framework for achieving this end. In so doing, we show that a network perspective extends prison ethnographies by allowing quantitative assessment of prison culture and illuminating basic characteristics of prison social structure that are essential for improving inmate safety, health, and community reentry outcomes. We conclude by demonstrating the feasibility and promise of inmate network research with findings from a recent small-scale study of a maximum-security prison work unit.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".