Investigating Offending Consistency of Geographic and Environmental Factors Among Serial Sex Offenders
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
Crime linkage analysis constitutes a tool to help investigators prioritize suspects, but a scarcity of research and methodological issues limits our knowledge on behavioral consistency in sexual offenses. The current study identifies geographic and environmental factors that are useful in examining offending consistency across series of sexual assaults using different specialization coefficients. The current study draws on criminal career research and methodology as a way to improve the study of behavioral consistency. The sample includes 72 serial stranger sex offenders who have committed a total of 361 sexual assaults. Three methods are used (i.e., diversity index, forward specialization coefficient, and Jaccard’s coefficient) and reveal a high degree of offending consistency. All three methods also highlight promising factors to rely on for crime linkage of serial sexual offenses. Empirical and methodological implications for behavioral consistency research are discussed as well as practical implications for police investigations and crime linkage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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