Bibliographic record
Abstract
Purpose Factors influencing crime location choices are not only significant to rape investigations, but they are especially important for geographic profiling. The purpose of the current study is to use temporal, hunting behavior, and modus operandi factors to determine those variables that influence the victim encounter and release locations in serial sexual crime. Design/methodology/approach Due to the possible correlated nature of serial rapes, the authors use generalized estimating equations (GEE) on a sample of 361 rapes committed by 72 serial sex offenders. Findings Results indicate that temporal factors, offender hunting behavior, and modus operandi strategies are significant predictors of both the victim encounter and release sites, but the importance of these factors varies depending on whether the location is in a residential land use area, a private site, inside location, or a site that is familiar to the offender. Practical implications Police can learn from the current findings and apply them to subsequent rapes within a series by recognizing the timing of the offense, the type of hunting pattern and attack method used in prior sexual crimes committed by the same offender, and modus operandi strategies, to determine the type of location where the rapist is likely to offend next. Originality/value This paper is the first attempt to predict factors related to both the encounter and the victim release site in serial rapes using GEE.
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.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".