Sexual Homicide: An Updated Analysis of the Offender, the Victim and the Offence
Bibliographic record
Abstract
Partly due to the brutality of the acts involved (e.g., mutilation of genitalia, dismemberment, foreign object insertion) as well as the apparent randomness of victim selection, sexual homicide is a crime that has always attracted attention from scholars, clinicians, and the police. When looking specifically at the empirical research on this type of crime, we can identify three distinct periods. The first period can be traced back to the early work of Krafft-Ebing. This period was mainly characterized by descriptive or case studies from clinical observations. Although interesting and informative, it was difficult – even impossible – to generalize the findings. The second period saw the proliferation of quantitative studies based on small samples. The pioneer study from the FBI marks the beginning of this period which saw the publication of several typologies as well as comparative studies between sexual homicide offenders (SHOs) and nonhomicidal sex offenders (NHSOs). Research from this period allowed to empirically test some of the ideas suggested in older clinical studies, as well as to build a knowledge base on sexual homicide, something that criminology had been reluctant to do (DeLisi & Wright, 2014). However, these studies were based on relatively small samples that were not representative. Recently, we have entered a third period characterized by empirical research based on large and representative samples. From studies based on 36 SHOs, some of the latest research was undertaken based on samples from 350 to almost 800 cases. Beyond the actual number of cases, these studies have allowed to question some of the findings that emerged from the second period. Furthermore, we have observed a desire from researchers in this field to collaborate between them. Such collaboration has culminated in the publication of the Routledge International Handbook of Sexual Homicide Studies (Proulx, Beauregard, Carter, Mokros, Darjee, & James, 2018) as well as the creation of the Sexual Homicide International Database (SHIelD; Chopin & Beauregard, 2019). Considering the evolution of sexual homicide research over the years, it felt necessary to update what is known on the SHO and his offense. We have identified different areas where significant developments were made, more specifically on the following issues: SHO as a unique type of sex offender; Heterogeneity of sexual homicide; Vulnerable victims in sexual homicide; Sadism and psychopathy and how they relate to sexual homicide, and; International comparisons on sexual homicide. Unfortunately, it was not possible to address all the interesting research that has been produced over the past few years. However, we have targeted some of the most important issues related to the SHO and his crime.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".