Predictors of recidivism by stalkers: A nine‐year follow‐up of police contacts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a subsample of a multisite stalking study (Mohandie, Meloy, McGowan, & Williams, 2006) comprising 78 offenders from one site, 77% committed new offenses within an average follow-up of 106 months (8.8 years). Over half (56%) were charged for new stalking related offenses and 33% for violent recidivism. Violent reoffending, including sexual offenses, was predicted by risk factors consistent with existing literature: younger age at first conviction, prior release failures, and criminal history. Stalking recidivism was predicted by pre-index offending scores, using the Cormier-Lang, and prior diagnosis of a mental illness. In addition, stalkers with previously diagnosed mental illness had significantly more police contacts as complainants than those without; their recidivism was also more likely to be non-violent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it