Femme-bracing Neurodiversity: Applying Femme Theory to the Experiences of Feminine Autistics
Bibliographic record
Abstract
Abstract At the crossroads of femme theory and Autistic femininity lie the nuanced experiences of individuals whose identities challenge societal norms of both gender and neurodiversity. By delving into these intersections, this article seeks to illuminate the unique perspectives of Autistic individuals who, in some aspect, identify with femininity. Employing six core principles of femme theory—reclaiming femininity, valuing feminine knowledge and characteristics, intersectionality, agency and empowerment, visibility and inclusivity, and resistance to femmephobia and misogyny—, the authors analyze, through theory application, how these principles manifest in the lives of Autistic individuals. Using femme theory, this research identifies the nuanced ways Autistic individuals navigate societal expectations and stereotypes. The findings contribute to a more comprehensive understanding of the diverse spectrum of femininity, emphasizing the importance of recognizing and respecting the agency and experiences of Autistic individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".