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Record W4401699460 · doi:10.1080/00918369.2024.2392681

Local Dynamics of Intersectional Stigma for Black LGBTQ People in Montreal, Quebec

2024· article· en· W4401699460 on OpenAlexafffundabout
Darius Scott, Eric Bird

Bibliographic record

VenueJournal of Homosexuality · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsStigma (botany)IntersectionalityGender studiesBlack womenQueerMale HomosexualitySociologyMen who have sex with menPsychologyMedicineHuman immunodeficiency virus (HIV)Psychiatry

Abstract

fetched live from OpenAlex

Much academic literature on intersectional stigma is limited by a focus on relatively static and "universal" identity traits, such as ethnicity, gender, and sexuality. This paper addresses local dynamics of intersectional stigma for Black LGBTQ people in Montreal, QC, Canada. Findings draw from fourteen semi-structured, virtual interviews with key informants providing critical services to Black LGBTQ people living in Montreal. Findings suggest intersectional stigmatization via social identity and local power dynamics converge. Specifically, language and immigration are two domains determining intersectional stigma challenges and ameliorative opportunities for Black LGBTQ people in the city. Specific immigration-related challenges included (1) insecurity (e.g. concerning Canadian residency), (2) barriers to resource access (e.g. social and legal services), and (3) stressful identity challenges. Specific language issues included (1) Francophone limitations for expressing gender and sexual diversity and (2) exclusionary linguistic divisions (i.e. Franco/Anglo, Franco/non-Franco, and Western/non-Western). Local, place-based power inequities may determine black LGBTQ experiences of intersectional stigma.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.910

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 studies0.0000.000
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 head0.031
GPT teacher head0.386
Teacher spread0.356 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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