An Empirical Comparison of the Profiles of Security Threat Group Offenders with General Offenders
Bibliographic record
Abstract
Datasets of offender attributes, both pre-custody and in-custody, were provided by the Correctional Service of Canada with the goal of exploring whether Security Threat Group (STG) offenders (informally, gang members of various kinds) differ in any systematic way from other offenders. For pre-custody attributes, we show that the entire offender population varies along two almost independent axes, one associated with affinity for violence, and the other with affinity for substance abuse. Within this structure, STG offenders are characteristically less extreme, in either direction, than the general offender population. For approximately two dozen attributes, STG offenders, as a group, tend to have higher values; for a few, they tend to have lower values. For in-custody attributes, the entire offender population forms a triangular structure whose vertices represent: passivity; violence and troublemaking; and involvement in programs leading to partial release. The differences between the STG offender population and the general offender population are small. An offender who is placed at the high end of the propensity for violence axis and/or the high end of the substance abuse axis based on pre-custody attributes is much more likely to be involved in incidents, grievances, and violence while in custody. This may have implications for risk stratification of incoming offenders.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".