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Record W2520236144 · doi:10.1177/1097184x16664951

“I Walked into the Industry for Survival and Came Out of a Closet”

2016· article· en· W2520236144 on OpenAlexafffundabout
Premala Matthen, Tara Lyons, Matthew Taylor, James Jennex, Solanna Anderson, Jody Jollimore, Kate Shannon

Bibliographic record

VenueMen and Masculinities · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
FundersCanadian Institutes of Health ResearchNational Institute on Drug AbuseCanada Research ChairsMichael Smith Health Research BC
KeywordsClosetSex workGender studiesThematic analysisSex workersDiversity (politics)TransgenderLesbianSociologyGender diversityQueerNarrativePerspective (graphical)PsychologyQualitative researchHuman immunodeficiency virus (HIV)DemographyGeographyMedicineSocial sciencePopulationManagement

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVES: This paper seeks to examine how gender and sexual identities shape sex work experiences among men, two spirit, and/or trans people in Vancouver. METHODS: In-depth, semi-structured interviews were conducted with men and trans people in Metro Vancouver from CHAPS (Community Health & HIV Assessment of Men Who Purchase and Sell Sex). An intersectional critical feminist perspective guided the thematic analysis of interview transcripts, and ATLAS.ti 7 was used to manage data analysis. RESULTS: Three themes emerged from the data: (1) the diversity of sexual and gender identities among sex workers and clients, (2) the expression and exploration of sexual and gender identities through sex work, (3) the migration of sexual and gender minorities to urban centres to escape discrimination in their places of origin. DISCUSSION: These findings complicate existing narratives of sex work, demonstrating the need for policies and services that reflect the diversity of sex work experiences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0290.015
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0200.005

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 head0.310
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2016
Admission routes3
Has abstractyes

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