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Record W7037935675

Exploring resilience among queer youth in gender sexuality alliances

2023· dissertation· en· W7037935675 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsQueerQueer theoryContext (archaeology)Human sexualityResilience (materials science)Psychological resilienceHeteronormativity
DOInot available

Abstract

fetched live from OpenAlex

Gender Sexuality Alliances (GSAs) are an important vehicle for engaging queer youth in Canadian high schools. GSAs present a useful entry point for queer pedagogy and can counter heteronormative discourse in schools. The literature shows that GSAs are an important tool for building resilience in queer youth. However, few studies have been undertaken that examine the work and impact of GSAs from the perspective of both queer theory and resilience theory. Drawing on queer theory and resilience theory, this case study examines the experiences of three Manitoba high school students in order to examine how queer theory and resilience theory speak to each other in the context of GSAs in Manitoba secondary schools. This research project addresses the following main research questions: 1) How do student experiences of GSAs align with both queer theory and resilience theory? 2) How do students who participate in GSAs perceive their resilience? Interviews showed that participants experienced their GSAs as spaces where they could safely socialize and explore their identity, but did not engage in broader educative or advocacy activities. These findings point to implications and recommendations for GSA advisors and school administrators in order to help GSAs better serve queer youth in secondary schools.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.175
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
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 head0.160
GPT teacher head0.329
Teacher spread0.169 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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