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Record W2982387327 · doi:10.1080/13691058.2019.1679393

Money boys in Chengdu, China: migration, entrepreneurial precarity and health service access

2019· article· en· W2982387327 on OpenAlexaff
Anthony Huynh, Nancy Yu, Juying Zhang, Claudyne Chevrier, Lisa Lazarus, James Blanchard, Robert Lorway

Bibliographic record

VenueCulture Health & Sexuality · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPrecarityChinaService (business)BusinessEconomic growthDemographic economicsPolitical scienceSociologyGender studiesEconomicsMarketing

Abstract

fetched live from OpenAlex

This qualitative study highlights the complex interplay between the social and structural conditions in Chengdu, China that shape the possibilities and vulnerabilities of money boys' sexual health. Within the context of China's liberalised market economy, we explore (1) how money boys' enter the sex trade market and navigate their sexual networks; (2) how their lives are enmeshed in fields of sexual desire, stigma and coercion; and (3) how the illicit and stigmatising nature of their work poses barriers to health service access. Findings reveal how the sex trade market and clinic are precarious spaces in which entrepreneurial ethics of the self and stigma-related coercive relations simultaneously enable and constrain money boys' sexual freedom and safer sex practices. By understanding this entrepreneurial precarity through the co-articulation of clinical and organisational work spaces, public health and social service providers can have a stronger sense of how various vulnerabilities configure to affect safer sex practices.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.402
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 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

Citations8
Published2019
Admission routes1
Has abstractyes

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