The development and validation of the guidelines for stalking assessment and management
Bibliographic record
Abstract
International research has established that stalking is a prevalent problem with serious and often life-threatening consequences for victims. Stalking is also a unique form of violence due to its nature and diversity, making it difficult for criminal justice and health professionals to establish which perpetrators and victims have the greatest need for services and protection. Risk assessment is one way to address these problems but few tools exist. This article describes the development of the Guidelines for Stalking Assessment and Management (SAM), the first risk assessment instrument designed specifically for the stalking situation. Preliminary data are presented, indicating that the SAM has promise for use by professionals working with stalkers and their victims. Results indicated that interrater reliabilities for the SAM risk factors and total scores range from fair to good, and the structural reliability of the SAM is sound. Moreover, the SAM showed good concurrent validity when compared with two other measures of violence propensity: the Psychopathy Checklist Screening Version (PCL:SV) and the Violence Risk Appraisal Guide (VRAG). Limitations of the study are discussed, especially those related to the difficulties inherent in file-based research, and suggestions for future research are offered.
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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 | 0.076 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".