While research confirms that asexual adolescents and youth face significant discrimination, scholarship frequently treats this population as a monolith. This homogeneous assumption obscures the profound internal heterogeneity of their experiences, rendering invisible the structural inequalities within the community. This study integrated latent class analysis (LCA) and an intersectional framework to provide a more nuanced understanding of this oppression. My objectives were to 1) identify distinct, qualitatively different classes of discrimination and 2) examine how an individual's membership in these classes is structured by the mutual constitution gender identity and outness. Using a sample of 14,304 asexual adolescents and youths aged 25 and younger from the pooled 2021-2022 ACE Community Surveys, I applied LCA to 11 discrimination indicators. I then used multinomial regression to predict class membership based on the intersection of gender identity and outness. LCA identified four distinct classes: "Low Discrimination" (45.6%), "Asexual-Specific Invalidation" (23.3%), "Interpersonal Victimization & Microaggressions" (12.4%), and "Pervasive Discrimination" (18.7%). Regression analyses confirmed a non-linear relationship between outness and discrimination risk. Critically, intersectional analysis revealed that gender identity moderates this relationship. Transgender and non-binary (TNB) adolescents and youths exhibited a significantly higher baseline risk of asexuality-related discrimination. While increased outness escalated risk for all groups, "out" transgender adolescents and youths were the most vulnerable. Findings demonstrate that discrimination is far from a monolithic experience. The structural location of TNB identity establishes a high-risk baseline that alters the consequences of outness. Interventions must move beyond "one-size-fits-all" models to provide support tailored to adolescents and youths at these distinct intersectional locations.
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Zékai Lu
The Journal of Sex Research
McGill University
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Zékai Lu (Sun,) studied this question.
www.synapsesocial.com/papers/69ddda4de195c95cdefd7b82 — DOI: https://doi.org/10.1080/00224499.2026.2656487