Validating the Predictive Accuracy of the Static Factors Assessment (SFA) Risk Scale for Federally Sentenced Offenders in Canada
Bibliographic record
Abstract
The Static Factors Assessment (SFA) is used by the Correctional Service of Canada to assess criminal risk. It includes 137 items in three sub-components: the Criminal History Record (CHR), Offence Severity Record (OSR), and Sex Offence History Checklist; the first two sub-components are examined in this study (109 items). Although the SFA has been used for all federal offenders for nearly 20 years, there are no studies examining its ability to predict community outcomes. This study included 8,767 federal offenders within a five-year follow-up period, and it examined revocations without an offence, readmissions for any offence, and readmissions for a violent offence. The overall SFA, CHR, and OSR were related to recidivism outcomes, although the sum of the items in the CHR significantly out-predicted the overall SFA rating. Most items in the CHR had significant predictive accuracy, whereas roughly half the OSR items were predictive; nonetheless, the OSR added positive incremental validity to the CHR. The SFA overall rating and the CHR and OSR sub-components are valid for offender risk assessment with Canadian federal offenders, although the current results suggest that improvements to the SFA should be undertaken.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".