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Record W4322744627 · doi:10.1177/08861099231155887

A Structural Analysis of Gender-Based Violence and Depression in the Lives of Sexual Minority Women and Trans People

2023· article· en· W4322744627 on OpenAlexafffundabout
Charmaine C. Williams, Margaret F. Gibson, Emily Mooney, Joellean R. Forbes, Deone Curling, Datejie Cheko Green, Lori E. Ross

Bibliographic record

VenueAffilia · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsPublic Health OntarioUniversity of WaterlooUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsOppressionStructural violenceGender studiesContext (archaeology)SociologySexual violenceNarrativeCriminologyTransphobiaHarmForegroundingPovertyPsychologySocial psychologyTransgenderPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article explores structural mechanisms that are the context for violence and depression in the lives of sexual minority women and trans people in Ontario, Canada. The article draws on interviews with 14 people who reported experiences of depression in the previous year, foregrounding three representative narratives. Narrative and case study analysis reveal that violence is a repeated and cumulative experience over lifetimes, occurring across different interpersonal contexts and institutional encounters. A common theme across the narratives is that experiences of violence are connected to a broader context in which structural arrangements, cultural norms, and institutional processes create conditions where marginalized people are put in harm's way, perpetrators are empowered, and justice and access to help are elusive. As the violence experienced by these sexual minority women and trans people is rooted in structural and cultural oppression represented in poverty, racism, misogyny, homophobia, and transphobia, the prevention of violence and its consequences for these and other marginalized populations requires systemic transformation of the structures and systems that currently allow and perpetuate harm.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.351
Teacher spread0.314 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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