Multidimensional and Intersectional Gender Identity and Sexual Attraction Patterns of Adolescents for Quantitative Research
Bibliographic record
Abstract
To identify and compare gender identity and sexual attraction (GISA) patterns using a latent class analysis (LCA), questionnaire data from a cross-sectional study on social resilience in adolescence was conducted in 2020, using a sample of 785 Swiss seventh grade high school students. Following McCall’s complex intersectionality approach, we applied an intracategorical and intersectional approach to reshape, differentiate, and critique the existing binary, heteronormative GISA categorization. To empirically validate the detected classes according to content, we measured the participants’ psychological characteristics with measures of self-esteem, social competence, symptoms of anxiety and depression, dissociation, social desirability, and emotional styles, and related these measures to the respective GISA patterns the LCA detected. The results of our multistep LCA endorsed that heteronormatively binary gender identities are far too simplistic to fully illustrate adolescents’ differences and similarities where gender is concerned. Out of the subsample of n = 785 adolescents (375 identified as “assigned females” and 410 “assigned males”), three significant subgroups of multidimensional GISA patterns emerged for both assigned females and males where differences within the identified GISA groups were larger than those between traditional “boys” and “girls” overall. The LCA demonstrated that the six classes with GISA indicators could be described as low GISA diverse ( cis /heterosexual), intermediate GISA diverse (gender identity diverse and/or sexual diverse), high GISA diverse (gender diverse/sexual diverse) for both assigned males and females thus showing that GISA and the psychological state according to gender variance is greater within groups of assigned females and assigned males than between these groups.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".