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Record W4288365180 · doi:10.5281/zenodo.4163718

Elderly sexual abuse: An examination of the criminal event

2019· article· en· W4288365180 on OpenAlexaff
Julien Chopin, Éric Beauregard

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCriminologyEvent (particle physics)Sexual abusePsychologySexual assaultMedical emergencyMedicineSuicide preventionPoison controlPhysics

Abstract

fetched live from OpenAlex

The current study investigates the modus operandi specificities for the sexual abuse against the elderly. A comparison between sex crimes against adult and elderly victims is conducted following the criminal event approach. The comparison is based on the precrime, crime, and postcrime phases of the modus operandi, operationalized through 53 variables. The sample comes from a French national police database including a total of 1,829 cases—including 130 cases of elderly sexual abuse and 1,699 cases of sexual abuse against victims aged between 18 and 45 years. Bivariate and multivariate analyses are performed to examine the differences in the two groups. Several differences are observed between the two modus operandi. Findings indicate that the precrime phase is the most important to explain these differences, and this phase of the criminal event affects the rest of the decisions taken during the crime and postcrime phases. Specifically, we have highlighted that sexual crimes against the elderly are more violent and occur more often in the victim’s residence. This study suggests that offenders targeting the elderly use specific crime characteristics, and this allows to highlight practical implications in terms of investigation and offender management.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.039
GPT teacher head0.287
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicElder Abuse and NeglectFrench-language works237,207