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When failure is the option: Unravelling sexual assault outcomes

2024· article· en· W4403048567 on OpenAlexaff
Julien Chopin, Éric Beauregard, Amélie Pedneault

Bibliographic record

VenueJournal of Criminal Justice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsSimon Fraser UniversityUniversité Laval
Fundersnot available
KeywordsSexual assaultPsychologyCriminologyMedical emergencyMedicineComputer securityPoison controlHuman factors and ergonomicsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.378
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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