Les agressions sexuelles d'enfants non résolues par la police : Une analyse du processus de passage à l'acte
Bibliographic record
Abstract
Sexual assaults against children are considered as one of the most serious types of aggression. Despite significant human and monetary resources being made available to crime investigators, some of these crimes still remain unsolved. The non-discretionary perspective suggests that offenders may adopt certain behaviors that increase their chances of avoiding detection. This study aims to explore the role of the offender’s choices and behavior (i.e., selection of certain victim characteristics, crime location parameters, crime characteristics, and forensic awareness strategies) on crime solving (i.e., solved vs. unsolved). This research is based on a sample of 309 rape cases (200 solved and 109 unsolved) involving child victims occurring in France between 1982 and 2015. The sample was restricted to victims aged less than 16 years old, and who did not know their perpetrator at the time of the offense. Bivariate and multivariate analyses indicate that selection of certain victim characteristics (e.g., unsupervised victims), crime location parameters (e.g., encounter, crime, and victim release took place at the same location), and offender behavior (e.g., diversity of sexual acts; strategy of approach) impacted crime solving. This study also shows that most child abusers do not use forensic awareness strategies to avoid police detection. This study presents both theoretical and practical implications in terms of criminal behavior understanding and criminal investigation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".