Predicting Physical Violence Against Corrections Officers Across Three Levels of Severity Using Individual and Environmental Characteristics
Bibliographic record
Abstract
Working in prisons can be a challenging job, managing a population of incarcerated people while keeping oneself, one's colleagues, and the people themselves safe. Some corrections officers may expect violence in the workplace, yet being a victim of violence is no trivial experience. In prison, violent incidents are categorized according to the severity of the violence perpetrated. However, we do not know how characteristics of a violent incident may contribute to the severity of violence perpetrated toward corrections staff. To begin to address this gap, we examined characteristics of physical assault incidents in New Zealand prisons between 2016 and 2020, in which the perpetrator of the incident was a male prisoner and the victim was a corrections officer. We examined the prediction of incidents across three levels of severity using individual and environmental characteristics. Perpetrators of serious violence tended to be already segregated from the general population at the time of the assault. We also found that perpetrators of assault against staff were different from the general prison population: prisoners who assaulted staff were more likely to be younger, gang affiliated, and had higher security classifications compared to prisoners who did not assault staff. Research suggests that characteristics of perpetrators can contribute to their risk of perpetrating violence; we found that characteristics of perpetrators (i.e., being segregated) can also contribute to the severity of violence perpetrated. Furthermore, we offer a direct comparison between prisoners who assaulted staff and prisoners who did not, therefore cementing research that prisoners who assaulted staff are different from the rest of the prison population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".