Contributions of Psychological Science on Response to Sexual Assault
Bibliographic record
Abstract
Sexual offending represents one of the most difficult crimes to investigate by the police due in part to the various complexities involved. For instance, contrary to most forms of crime, the sole presence of forensic evidence (e.g., DNA) is often not enough to charge and convict a suspect. In many cases, the notion of consent will need to be debated, which more often than not, comes down to the word of the victim against the offender’s. Therefore, it becomes of the utmost importance to develop various techniques that can be used by the police in their effort to find the truth and charge the right suspect. Although the field of research on sexual violence has traditionally focused its efforts on improving our understanding of the various risk factors related to this form of offending as well as how to best treat and manage these offenders, some researchers have conducted innovative research applied specifically to the investigation of sexual crimes. The aim of this chapter is to review some of the most important findings on sexual offending that can be applied to the police criminal investigation. More specifically, the chapter will start by reviewing some of the misconceptions about “sex offenders” that may mislead an investigation. Then, we will discuss the various studies on suspect prioritization and crime linkage analysis, presenting the best practices. The chapter will also cover the investigative interviewing specific to sexual crimes, focusing on the witnesses, the victims, and the suspects. Moreover, related to interviewing, the chapter will review and discuss the indicators of false rape allegations as well as false confessions. The chapter will review some of the proactive strategies used in sexually-related online crimes and will end with a review of how risk assessment
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".