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Record W2041787166 · doi:10.1080/13691058.2011.626454

Changing behaviours and continuing silence: sex in the post-immigration lives of mainland Chinese immigrants in Canada

2011· article· en· W2041787166 on OpenAlexafffundabout
Yanqiu Zhou

Bibliographic record

VenueCulture Health & Sexuality · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsImmigrationHuman sexualityMainland ChinaOpenness to experienceChinaGender studiesVulnerability (computing)Reproductive healthObsolescenceMainlandSociologyPolitical scienceDevelopment economicsPsychologyGeographySocial psychologyDemographyPopulationBusiness

Abstract

fetched live from OpenAlex

In China, reluctance to discuss sex continues to be widely observed despite the sexual revolution there. That silence generates questions about health risks in the contexts of HIV/AIDS and international migration. Based on a qualitative study of mainland Chinese immigrants in Canada, this paper explores the impacts of immigration processes on sex and sexuality. The findings reveal a gap between these individuals' changing sexual behaviours and the continuing silence on sex. Although Canada has exposed them to a new living environment that has shaped the dynamics and patterns of their sexual practices, their incomplete integration into the host society and their close connections with China as the home country mean that traditional Chinese norms continue to influence their understanding of these changes. With the increasing openness of these immigrants' sexual relationships, the obsolescence of their consciousness and knowledge of sexuality should be addressed in order to reduce their vulnerability to sexual inequalities and consequent health risks.

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: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0190.009
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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

Citations28
Published2011
Admission routes3
Has abstractyes

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