Bibliographic record
Abstract
Purpose This paper aims to explore the presence of overkill in sexual homicide. More specifically, the study examines whether overkill is a valid indicator of an organized or disorganized sexual homicide. Moreover, the study tests the presence of various patterns of sexual homicide involving overkill. Design/methodology/approach The sample used in this study consists of 662 cases of extrafamilial SHs with ( n = 145) and without ( n = 517) evidence of overkill, respectively. A binomial regression was used to compare at the multivariate level the two groups of crimes, while a latent class analysis was used to determine whether overkill could be associated with different patterns of sexual homicide. Findings Findings from bivariate and logistic regression analyses show that the presence of overkill may be associated with both organized and disorganized sexual homicides. Moreover, latent class analysis suggests that there are three distinct patterns of overkill in sexual homicide: impulsive, sadistic and personal. Originality/value This study is the first to empirically analyze overkill in sexual homicides and to propose a classification using crime-commission process characteristics.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".