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Record W1978128803 · doi:10.1080/13691058.2012.738310

Challenging homophobia and heterosexism through storytelling and critical dialogue among Hong Kong Chinese immigrant parents in Toronto

2012· article· en· W1978128803 on OpenAlexaffabout
Josephine Pui‐Hing Wong, Maurice Kwong-Lai Poon

Bibliographic record

VenueCulture Health & Sexuality · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork UniversityToronto Metropolitan University
Fundersnot available
KeywordsHeterosexismGender studiesLesbianStorytellingVictimisationImmigrationLiminalityHuman sexualitySociologyDialogical selfHomosexualityPsychologySocial psychologyPolitical scienceNarrativePoison controlSuicide prevention

Abstract

fetched live from OpenAlex

Homophobia and heterosexism are ubiquitous in Canadian society. They contribute to significant health and mental health disparities for lesbian, gay and bisexual youth and their families. Anti-homophobia efforts tend to focus on students and teachers at school. While these efforts are important, they do not reach parents, who play an important role in shaping young people's attitudes towards gender and sexuality. To eliminate bullying and victimisation associated with homophobia at school and in the community, concerted efforts are urgently needed to mobilise parents to become champions against homophobia and heterosexism. In this paper, we report on our use of storytelling and critical dialogue to engage a group of Hong Kong Chinese immigrant parents in Toronto to interrogate their values and assumptions about homosexuality. In particular, we illustrate how we use storytelling to create a liminal space whereby the narrators and listeners collaborate to create counter-discourses that challenge social domination and exclusion. We then discuss the implications of using a critical dialogical approach to integrate anti-homophobia efforts in community parenting programmes.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.064
GPT teacher head0.456
Teacher spread0.393 · 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.

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

Citations24
Published2012
Admission routes2
Has abstractyes

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