Network integration within a prison-based therapeutic community
Bibliographic record
Abstract
Prison-based therapeutic communities (TCs) are a widespread, effective way to help incarcerated individuals address substance abuse problems. The TC philosophy is grounded in an explicitly relational paradigm that entails building community and conditioning residents to increasingly take responsibility for leadership therein. Although TCs are based on cultivating a network that continuously integrates new residents, many common structural features can jeopardize TC goals and are hence discouraged (e.g., clustering, homophily). In light of this tension, analyzing the TC from a network perspective can offer new insights to its functioning, as well as to broader questions surrounding how networks integrate new members. In this study we examine a men's TC unit in a Pennsylvania prison over a 10-month span. Using data on residents' informal networks, we examine: (1) how well individuals integrate into the TC network across time, (2) what predicts how well residents integrate into the TC, and (3) how well the TC network structure adheres to theoretical ideals. Results suggest that individual integration is driven by a range of hypothesized factors and, with limited exceptions, the observed TC is able to foster a network structure and integrate residents consistent with TC principles. We discuss the implications of these results for evaluating TCs and for understanding the process of network integration.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".