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Record W4321495829 · doi:10.1177/02697580231154941

Less exposed, more vulnerable? Understanding the sexual victimization of women with disabilities under the lens of victimological theories

2023· article· en· W4321495829 on OpenAlexaff
Julien Chopin, Éric Beauregard, Nadine Deslauriers‐Varin

Bibliographic record

VenueInternational Review of Victimology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversité LavalInternational Centre for Comparative CriminologySimon Fraser University
Fundersnot available
KeywordsVulnerability (computing)PsychologyVictimologySexual abuseDevelopmental psychologyBivariate analysisPoison controlSexual assaultInjury preventionMedicineMedical emergencyComputer security

Abstract

fetched live from OpenAlex

This study aims to examine the sexual victimization process of individuals with disabilities using the interactional victimology theoretical framework. Specifically, we compare the victimological indicators of four different situations: victims were not disabled, victims were physically disabled, victims were psychologically disabled, and finally, victims were both physically and psychologically disabled. The sample used in this study consists of 1,077 cases of extrafamilial sexual assaults involving adult victims in France. Bivariate and multivariate analyses are performed to examine the differences between cases where victims were not disabled ( n = 500), victims were physically disabled ( n = 243), victims were psychologically disabled ( n = 276), and victims were both physically and psychologically disabled ( n = 58). Findings show that disability is a factor increasing the severity of sexual violence and that the type of disability affects the parameters of the victimization process. Moreover, results show that sexual victimization of persons with disabilities is more likely due to their vulnerability than to their exposure to risks. Both theoretical and practical implications related to the vulnerability concept are discussed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.083
GPT teacher head0.368
Teacher spread0.285 · 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.

Study designTheoretical or conceptual
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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

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