Included but Not Inclusive
Bibliographic record
Abstract
Abstract Recent research has highlighted increased gender diversity among Autistic individuals, particularly those raised as girls. Femme theory challenges traditional femininity, offering an inclusive, intersectional lens on identity. This study uses femme theory to critique literature on the sexual and gender experiences of Autistic individuals feminized by their sex assigned at birth or gender (women, girls, females) and feminine others, examining the association between gender norms and feminine Autistic experiences. Researchers conducted a critical content analysis of qualitative articles published on the experiences of Autistic individuals on the feminine spectrum from three prominent Autism journals (2019–2022), using femme theory to analyze dominant narratives and themes. Findings revealed two overarching themes: divergenceand alignment with femme theory. Key themes included inadequate examination of femininity, limitations in discussing gender diversity, and recognition of intersectionality. The study calls for more inclusive language, challenging stereotypes, and incorporating diverse perspectives. Future research should adopt broader, intersectional approaches to better represent feminine Autistic experiences.
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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 | 0.012 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.162 | 0.044 |
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".