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Record W3044255253 · doi:10.1080/15299716.2020.1791300

Young Bisexual People’s Experiences of Sexual Violence: A Mixed-Methods Study

2020· article· en· W3044255253 on OpenAlexaff
Corey E. Flanders, RaeAnn E. Anderson, Lesley A. Tarasoff

Bibliographic record

VenueJournal of Bisexuality · 2020
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsLesbianPsychologySexual minorityClinical psychologyVulnerability (computing)

Abstract

fetched live from OpenAlex

Bisexual people are at an increased vulnerability for sexual victimization in comparison to heterosexual people, as well as gay and lesbian people. As the majority of first sexual violence experiences happen prior to age 25 for bisexual women, young bisexual people are particularly vulnerable. Despite consistent evidence of this health disparity, little is known about what factors might increase young bisexual people's risk for sexual victimization, or how they access support post-victimization. The current study addresses this gap through a mixed-method investigation of young bisexual people's experiences of sexual violence with a sample of 245 bisexual people age 18-25. Quantitative results indicate that bisexual stigma significantly predicts a greater likelihood of reporting an experience of sexual violence. Qualitative findings support that while not all participants felt bisexual stigma related to their experience of sexual violence, some felt negative bisexual stereotypes were substantial factors. Interview participants found connecting with other survivors, particularly LGBTQ+ and bisexual survivors, to be beneficial. Some participants encountered barriers to accessing support, such as discrimination in schools. Sexual violence researchers should consider bisexual stigma as an important factor, and support services the potential positive impact of bisexual-specific survivor support.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.480
Teacher spread0.389 · 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 designObservational
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

Citations39
Published2020
Admission routes1
Has abstractyes

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