Article 6: Constructing Risk Assessment Algorithms For Violent, Drug And Property-Related Offenders: An Empirical Approach
Bibliographic record
Abstract
In the Canadian federal correctional system, the Offender Intake Assessment (OIA) and correctional planning process are primarily focused on addressing static and dynamic risk factors. These core components of OIA were examined to determine whether algorithmic equations tailored across major offence types could potentially be used for administration by means of a hand-held mobile application. In accordance with Schedules in the Corrections and Conditional Release Act, three major offence types, namely Schedule I (violent, excluding murder), Schedule II (drug), and Non-violent (property) were constructed for 6,525 male first releases over a two year period (2016-17 and 2017-18). An Offender Management System database was used to extract a set of 11 static risk indicators and 7 dynamic domain ratings for each case. Also gathered from OMS was whether or not there were any returns to federal custody. A combined static and dynamic risk index yielded impressive predictions of custodial return for violent, drug and property-related offenders with significant AUCs of .76, .70 and .71, respectively. These results suggest that combining static and dynamic factors into scoring algorithms for major offence types can be useful for moving offender risk assessment further towards streamlined applications technology.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".