Harassment Due to Gender Nonconformity Mediates the Association Between Sexual Minority Identity and Depressive Symptoms
Bibliographic record
Abstract
The visibility of a stigmatized identity is central in determining how individuals experience that identity. Sexual minority status (e.g., identifying as gay, lesbian, or bisexual) has traditionally been identified as a concealable stigma, compared with race/ethnicity or physical disability status. This conceptualization fails to recognize, however, the strong link between sexual minority status and a visible stigma: gender nonconformity. Gender nonconformity, or the perception that an individual fails to conform to gendered norms of behavior and appearance, is strongly stigmatized, and is popularly associated with sexual minority status. The hypothesis that harassment due to gender nonconformity mediates the association between sexual minority status and depressive symptoms was tested. Heterosexual and sexual minority-identified college and university students (N = 251) completed questionnaires regarding their sexual minority identity, experiences of harassment due to gender nonconformity, harassment due to sexual minority status, and depressive symptoms. A mediational model was supported, in which the association between sexual minority identity and depressive symptoms occurred via harassment due to gender nonconformity. Findings highlight harassment due to gender nonconformity as a possible mechanism for exploring variability in depressive symptoms among sexual minorities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".