Scripting Extrafamilial Child Sexual Abuse: A Latent Class Analysis of the Entire Crime-Commission
Bibliographic record
Abstract
Background: For several years, studies have produced general scripts of child sexual abuse. These scripts provided an analysis of each individual step of the crime-commission process, neglecting the connection between each of these steps. Objective: The purpose of this study is to use the crime script analysis to explore child sexual abuse as a whole, while considering the interconnectedness between each step involved in the crime-commission process of these offenders. Participants: This study analyzes the characteristics of 2264 cases of extrafamilial child abuses recorded by the French police between 1983 and 2018. Methods: The first step of this research uses latent class analysis to explore the relationship between each step of the crime-commission process. This statistical procedure allows for the identification of patterns in a set of data that share behavioral characteristics. In the second step, we used additional variables to test the external validity of our model. Results: Results suggest that there are four different scripts used by child sexual assault offenders related to criminal opportunities, crime preparation, and the crime commission process. Three scripts involved stranger offenders while only one involved acquaintance abusers. Conclusions: The analysis of different scripts shows that the relationship between offenders and victims, as well as the victim's profile in terms of opportunities (i.e., age and routine activities) influenced the decision-making process of child sexual abuse offenders. This new perspective in the crime script analysis of child sexual abuse allows for more tailored prevention measures.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".