Forecasting Stalking Recidivism Using the Guidelines for Stalking Assessment and Management (SAM)
Bibliographic record
Abstract
We examined the long-term risk for stalking recidivism and the predictive validity of ratings made using the Guidelines for Stalking Assessment and Management (SAM) in 100 stalking offenders from a forensic clinic. Overall, 45 offenders were convicted of, charged with, or the subject of police investigation for stalking-related offenses during a potential time at risk that averaged 13.47 years. Survival analyses using the Cox proportional hazards model indicated that a composite score of the presence of SAM risk factors was significantly predictive of recidivism and had significant incremental validity relative to total scores on two scales commonly used in violence risk assessment, the Screening Version of the Hare Psychopathy Checklist-Revised (PCL:SV) and the Violence Risk Appraisal Guide (VRAG). Overall ratings of risk made using the SAM, however, were not significantly predictive of recidivism. We discuss the potential uses of the SAM in stalking risk assessment and provide recommendations for future research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".