Can Stalking Be Used as a Risk Factor in Predicting Rates, Severity, and Frequency of Intimate Partner Violence Recidivism?
Bibliographic record
Abstract
Research has indicated that stalking is a prevalent crime in North America. Despite this concern, the current laws do not adequately protect victims from stalking recidivism. This contributes to victims often being revictimized. Moreover, victims may experience many detrimental concerns, particularly prolonged psychological and social problems. Stalking-related behaviours have also been associated with intimate partner violence (IPV). Specifically, stalking victims are at risk of experiencing more severe physical violence from a former partner. Although there are many validated IPV risk assessment tools such as the Ontario Domestic Assault Risk Assessment (ODARA), stalking risk tools have been found to be moderate, at best, in terms of their psychometric properties. The aim of this study is to examine whether IPV perpetrators who have stalked their victims differ from those who do not stalk in terms of recidivism risk, rates, frequency, and severity. Additionally, we examine whether stalking can be used as a risk factor to better predict the time until recidivism occurs. This study initially included 249 IPV cases that were reported to police in 2017. One hundred of these cases were selected for follow-up and were coded for recidivism outcomes. Results indicate that those who stalk differ on some recidivism variables from those who do not stalk. Moreover, the addition of stalking as a risk factor to the validated risk tool, ODARA, did not incrementally improve prediction of the time until recidivism occurred. The implications of these findings will be discussed. Department: Psychology Faculty Mentor: Dr. Sandy Jung
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".