I am Queer and Asian: The Crossroad of Race and LGBTQIA+ Identity among Queer Asian Canadian Youth and the Impacts of the COVID-19 Pandemic
Bibliographic record
Abstract
LGBTQIA+ Asian youth face unique challenges due to their intersecting identities. Utilizing Asian Critical Race Theory and the Integration Model of Stress and Trauma in LGBTQIA+ Asians, LGBTQIA+ Asian youth are likely to experience detrimental mental health outcomes due to the stigmatization within their Asian culture and racial discrimination from the LGBTQIA+ communities. Moreover, the COVID-19 pandemic exacerbated existing challenges because of the closure of community spaces and increased hate crimes against Asians. Nonetheless, research on the intersectional experiences of LGBTQIA+ Asians in Canada, especially in the context of the COVID-19 pandemic is sparse. Thus, to address this gap, I undertook eight focus group discussions with 30 queer Asian youth across Canada, alongside a quantitative questionnaire to explore the participants’ mental health and intersectional experiences. Quantitative data showed that participants were experiencing negative mental health outcomes (i.e., stress, anxiety, and depression). Thematic analysis showed despite negative impacts from the COVID-19 restrictions and increased anti-Asian sentiments, the restriction allowed youth to explore their sexual orientation and gender identity. Participants also shared that the movement #stopAsianhate was empowering but excluded queer Asian voices. Furthermore, similar to queer Asian Americans’ experiences reported in the literature, participants experienced exclusion from both the queer (e.g., queer racism) and Asian communities (e.g., cultural stigmas), which seemed to exacerbate mental health concerns. As LGBTQIA+ safe spaces lack cultural sensitivity and tended to be White-dominated, more resources and funding should be devoted to queer Asian communities to create safe spaces for themselves and also educate the public about their experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".