Nothing for us, except by us – Support for queer ethnic young people in Aotearoa New Zealand
Bibliographic record
Abstract
Purpose This paper looks at mainstream lesbian, gay, bisexual, transgender, queer, intersex and asexual (LGBTQIA+) support services in Aotearoa New Zealand, which predominantly center white queer voices and services and fail to account for the intersectional identities of young ethnic queers. Design/methodology/approach This exploratory, qualitative study investigated the social and professional support experienced and responded to by 43 young ethnic queers living in Aotearoa New Zealand, who were between 18 and 35 years of age. Participants identified as queer, non-binary, gay, pansexual, demisexual, gender fluid, non-binary and trans among others and held ethnic heritage from Africa, Asia, the Middle East and the Americas. Persadie and Narain's Mash Up methodological approach (2022) was used to analyze the data. Mash Up allowed us to understand the intersectional spaces of queer ethnic lives in white-dominated spaces, the ways in which young ethnic queers resisted the marginalization of their racialized being and took agency to counter actions and decisions that negated their presence and intersectional identities. Findings The findings from the study showed that young ethnic queers responded to the lack of adequate support services by establishing their own voluntary organizations and support networks. The study revealed that ethnic queer young people were critical of the white-dominated LGBTQIA+ support organizations; they created their own transformative spaces where they found “family” and community where they could be open about their queerness without the fear of rejection and stigma, while still advocating for equitable resources and an intersectional approach in queer mainstream services. Originality/value This paper provides valuable information on the lack of support for queer ethnic young people in Aotearoa New Zealand. The absence of information on the needs of this group poses a challenge to government departments, which rely on data to inform policy and allocate resources. The limited research and knowledge of this community make them less visible and, consequently, less likely to be given resources. It also means that harmful practices and behaviors toward queer ethnic young people by families and communities are more likely to go unnoticed and unaddressed. The paper also shows that the agency of young ethnic queers to create their own transformative spaces and to challenge the white-centric spaces, which have failed to consider their intersectional identities, has been instrumental to their well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".