Understanding Incarceration and Re-Entry Experiences of Female Inmates and Their Children: The Women's Prison Inmate Networks Study (WO-PINS), Pennsylvania, 2017-2018
Bibliographic record
Abstract
This study advances the understanding of incarceration and reentry, and their consequences for women by focusing on prison social systems and their informal network structures. The data for this project are aimed at four research questions: (1) What is the informal social structure within prison? (2) How are inmates' positions within the informal structure correlated with their health? (3) What are the consequences of informal social structure and inmates' positions within it for inmate-level and prison-level outcomes? and (4) How does in-prison and out-of-prison social capital correspond with community reentry and family reintegration? In phase 1, network data were collected for "get along with best" and "power and influence" nominations along with survey data to contextualize the measured networks. In phase 2, semi-structured interviews were conducted with eligible respondents to gather expectations for re-entry and anticipated egocentric support networks. Phase 3 followed paroled inmates for two subsequent interviews, and also gathered interviews with their children, and the children's caretakers. Administrative records were used to construct a recidivism supplement that is appropriate for modeling the hazard of recidivism following release. Behavioral data are combined from multiple sources, including inmate surveys, prison work records, misconduct records, drug tests, visitation lists, and gang classification data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".