Understanding Polyvictimization in Prison: Prevalence and Predictors Among Men Inmates in Spain
Bibliographic record
Abstract
Victimization—physical, sexual, and emotional—is part of prison life for a sizable proportion of incarcerated people. Research has primarily focused on the prevalence and predictors of physical or sexual victimization inside prisons located in the United States. Very little prison-based victimization research has been conducted in other countries, and even less has examined the clustering patterns of victimization (referred to as polyvictimization), and whether different demographic, behavioral health, and criminal risk factors are associated with polyvictimization. This article explores variation in victimization patterns during incarceration in Spain, and whether there is variation in the demographic, behavioral, and criminal risk factors predicting one type (physical, sexual, or emotional); two types (physical and sexual, physical and emotional, or sexual and emotional); or three types (physical, sexual, and emotional), as well as the number of different types of victimization distinguished by type of perpetrator. Self-report data were collected from 2,484 male inmates housed in eight adult prisons in Spain. More than half the sample reported at least one type of victimization, and one quarter reported two or more types of victimization. Polyvictimization was found to be strongly associated with prior childhood and adulthood victimization experienced in the community. These findings have significant policy and practice implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".