Where “Old Heads” Prevail: Inmate Hierarchy in a Men’s Prison Unit
Bibliographic record
Abstract
Research of inmate social order is a once-vibrant area that receded just as American incarceration rates climbed and the country's carceral contexts dramatically changed. This study reengages inmate society with an abductive mixed methods investigation of informal status within a contemporary men's prison unit. The authors collect narrative and social network data from 133 male inmates housed in a unit of a Pennsylvania medium-security prison. Analyses of inmate narratives suggest that unit "old heads" provide collective goods in the form of mentoring and role modeling that foster a positive and stable peer environment. This hypothesis is then tested with Exponential Random Graph Models (ERGMs) of peer nomination data. The ERGM results complement the qualitative analysis and suggest that older inmates and those who have been on the unit longer are perceived by their peers as powerful and influential. Both analytical strategies point to the maturity of aging and the acquisition of local knowledge as important for attaining informal status in the unit. In sum, this mixed methods case study extends theoretical insights of classic prison ethnographies, adds quantifiable results capable of future replication, and points to a growing population of older inmates as important for contemporary prison social organization.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| 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".