Giftedness, Gender Identities, and Self-Acceptance: A Retrospective Study on LGBTQ+ Postsecondary Students
Bibliographic record
Abstract
In a recent position statement, the National Association of Gifted Children argued the importance of providing equitable treatment of Lesbian, Gay, Bisexual, Transgender, Queer and other sexual and gender minority individuals (LGBTQ+) gifted youth to help them maximize their potential. However, there are very few empirical studies focusing on the intersection of giftedness and gender identities. Little is known regarding these students’ experience at, and outside of, school. Focusing on the individual process of gender identity development and self-acceptance, we interviewed nine LGBTQ+ postsecondary students in North America (aged between 19 and 29 years) who are graduates of an academically focused high school in Turkey. In particular, we studied their ways of thinking, stress coping strategies, and environmental factors that may have enabled their self-acceptance of LGBTQ+ identities. Findings of the study show that the mental health of LGBTQ+ is a function of individual factors (e.g., coping strategies), structural factors (e.g., a homophobic sociocultural environment), and the context. The findings also indicate the benefits of complexity and reflectiveness in thinking, metacognition and the ability to separate identity labels from identities, enabled by high school peer support, liberal curriculum and classroom discussions, and access to information during adolescence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".