When failure is the option: Unravelling sexual assault outcomes
Bibliographic record
Abstract
We investigate the concept of “criminal failure” in sexual crimes and the relevance of various theoretical frameworks for its understanding: individual offender's rational choice, environmental influences and routine activities, victimological characteristics from lifestyle theory, and crime interaction factors. We examined a sample of 1121 “failed” cases (i.e., attempted but not completed) and 1500 “successful” cases (i.e., completed) of sexual assault that occurred in France between 1990 and 2018. We used 32 predictors that mapped on the four theoretical frameworks and conducted bivariate followed by multivariate analyses. Multiple theoretical frameworks are relevant to understand criminal failure, which is a product of perpetrator, environmental, victimological, and interactional factors. Two distinct patterns are specifically associated with failure: lack of preparation and lack of social skills. In addition, failure was best understood not as a unitary concept, but as multifactorial by distinguishing between different types of failure, specifically: offender intentionally released the victim before completion, victim escaped or third party rescue. Finally, patterns of failure were different in sexual crimes against children compared to those against adults. Criminology should pay closer attention to failure in crime. This understudied area can yield important theoretical knowledge and practical implications regarding the prevention of sexual crimes.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".