MétaCan
Menu
Back to cohort
Record W2014398100 · doi:10.1080/13691058.2012.674158

The MaBwana Black men's study: community and belonging in the lives of African, Caribbean and other Black gay men in Toronto

2012· article· en· W2014398100 on OpenAlexafffundabout
Clemon George, Barry D. Adam, Stanley Read, Winston Husbands, Robert S. Remis, Lydia Makoroka, Sean B. Rourke

Bibliographic record

VenueCulture Health & Sexuality · 2012
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsSt. Michael's HospitalOntario HIV Treatment NetworkUniversity of WindsorAIDS Committee of TorontoPublic Health OntarioHospital for Sick ChildrenUniversity of TorontoSickKids FoundationUniversity of Ontario Institute of Technology
FundersCanadian Institutes of Health Research
KeywordsGender studiesBlack africanSociologyGeographyEthnology

Abstract

fetched live from OpenAlex

In Canada, there is a paucity of research aimed at understanding Black gay men and the antecedents to risk factors for HIV. This study is an attempt to move beyond risk factor analysis and explore the role of sexual and ethnic communities in the lives of these men. The study utilized a community-based research and critical race theory approach. Semi-structured interviews were conducted with eight key informants to augment our understanding of Black gay men and to facilitate recruitment of participants. In-depth interviews were done with 24 Black gay men. Our data showed that the construction of community for Black gay men is challenged by their social and cultural environment. However, these men use their resilience to navigate gay social networks. Black gay men expressed a sense of abjuration from both gay and Black communities because of homophobia and racism. It is essential for health and social programmers to understand how Black gay men interact with Black and gay communities and the complexities of their interactions in creating outreach educational, preventive and support services.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.431
Teacher spread0.357 · 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

Citations53
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueCulture Health & SexualitySame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207