“Calculated” risk-taking: the choice of risky locations in organized sexual homicide
Bibliographic record
Abstract
Purpose This study aims to examine the factors related to offender risk-taking in sexual homicide (SH). More specifically, this paper explores whether the crime-commission process of organized SH is associated with low-risk offending, operationalized as the frequency of selecting crime locations with the risk of witness detection. Design/methodology/approach The sample for this study consists of 350 cases of SH in Canada between 1948 and 2010. Bivariate analyses were used to identify significant differences between low-risk (n = 250) and high-risk (n = 100) offending across the independent variables. Moreover, logistic regression was used to determine under what conditions an organized crime-commission process is associated with low-risk offending. Findings The bivariate analyses indicate that many variables related to an organized crime-commission process were significantly associated with high-risk offending. Similarly, the logistic regression analyses demonstrated that behaviours linked to an offender’s sexual goals or fantasies (e.g. specific victim targeting, engaging in sexual intercourse with the victim, biting and leaving biological evidence) were associated with decreased odds of low-risk offending. In contrast, more opportunistic behaviours (e.g. selecting sex-trade workers and engaging in overkill) were associated with increased odds that the offender engaged in low-risk offending. Originality/value This study takes a unique approach to examining risk-taking in SH based on offenders’ selection of risky crime locations. It opens avenues for future research to explore different thresholds for risky offending behaviour and the decision-making that manifests in low- and high-risk crime-commission processes.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".