Sexual Crime Investigation and Offender's Decision-Making: Rationality, Achievement, and Expertise
Bibliographic record
Abstract
Criminal investigations typically oppose two main actors: the offender – taking precautions to avoid leaving evidence – and criminal investigators, who must act in light of the evidence at their disposal. Although successfully engaging in criminality does not require special skills, as evidenced by the lack of premeditation involved in most crimes, this apparent absence in decision making is not an indication of lack of skills and planning, but rather, it demonstrates that some offenders have developed in-depth knowledge and skills to assess various situations and opportunities – also known as criminal expertise. The notion of expertise in crime is directly linked to rational choice theory, as offenders develop skills to assess and respond to crime opportunities through practice. While assessing the risks and rewards associated with committing the crime, offenders will make a decision to act a certain way in order to improve the rewards while reducing the risks of getting caught. This chapter argues that similar to general offenders, sex offenders have developed a criminal expertise aimed at avoiding police detection. After discussing the concept of criminal expertise in general, the chapter examines this notion for sex offenders specifically. Moreover, the chapter reviews important notions related to offenders’ decision-making during the crime-commission process and how these may impact the ability of avoiding police detection. Finally, the chapter reviews how the investigation of sexual crimes may be partly influenced by criminal expertise.
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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 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".