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Record W4402465488 · doi:10.1007/s11199-024-01514-w

“You Did It to Yourself”: An Exploratory Study of Myths About Gender-Based Technology-Facilitated Violence and Abuse Among Men

2024· article· en· W4402465488 on OpenAlexafffundabout
Esteban Morales, Jaigris Hodson, Yimin Chen, Chandell Gosse, Kaitlynn Mendes, George Veletsianos

Bibliographic record

VenueSex Roles · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsWestern UniversityCape Breton UniversityRoyal Roads University
FundersSocial Sciences and Humanities Research CouncilRoyal Roads University
KeywordsPsychologyMythologyExploratory researchDevelopmental psychologyHuman factors and ergonomicsPoison controlSocial psychologyClinical psychologyMedical emergencySocial scienceSociology

Abstract

fetched live from OpenAlex

Gender-based technology-facilitated violence and abuse (GBTFVA) is a common experience for those engaging with digital technologies in their everyday lives. To better understand why GBTFVA persists, it is necessary to understand the false beliefs and cultural narratives that enable and sustain them. Drawing on the literature on rape myths, this paper explores the prevalence of seven gender-based online violence myths among Canadian men. To achieve this, we adapted the Illinois Rape Myth Acceptance (IRMA) (Payne et al., in J Research in Personality 33:27–68, 1999) to assess GBTFVA, and surveyed 1,297 Canadian men between 18 and 30 years old on their GBTFVA beliefs. Our results show that GBTFVA myths and cultural narratives are prevalent across participants, though endorsement levels vary. Four myths were more strongly endorsed: It Wasn’t Really Gender-Based Online Abuse , He Didn’t Mean To , Gender-Based Online Abuse Is a Deviant Event , and She Lied. Overall, these findings help to name and thus begin to address the narratives that sustain and perpetuate gender-based online violence.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.316
Teacher spread0.284 · 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

Citations7
Published2024
Admission routes3
Has abstractyes

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