Risks of Minor and Serious Violent Victimization in Prison
Bibliographic record
Abstract
The authors use the Survey of Inmates in State and Federal Correctional Facilities 2004, which includes 18,185 respondents, to examine why some inmates are more likely to experience violent victimization in prison, focusing on the effects of mental disorders, physical disabilities, and physical size. The authors test four hypotheses based on patterns of minor versus serious victimization and two mediators for provocative behaviors: verbal abuse and physical assault. (1) The authors do not find much support for the hypothesis that inmates with mental disorders, physical disabilities, or small physical size are stigmatized as being vulnerable. Inmates with symptoms of delusions are the only exception. (2) The authors do not find support for the hypothesis that inmates with mental disorders are labeled as being threatening, but they do find support for inmates with larger physical size. (3) The authors find support for the hypothesis that provocation contributes to the victimization of inmates with some types of mental disorders: personality disorders and paranoid thoughts. The victimization of inmates with other mental disorders is not explained by provocation, however. (4) The authors find tentative support for the hypothesis of reversed causality between some types of physical disabilities and serious violent victimization: Paralysis and brain injuries are probably consequences of serious violent victimization while in prison. In contrast, arthritis is a risk factor for victimization.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".