Gender differences in self-reported camouflaging in autistic and non-autistic adults
Bibliographic record
Abstract
Social camouflaging describes the use of strategies to compensate for and mask autistic characteristics during social interactions. A newly developed self-reported measure of camouflaging (Camouflaging Autistic Traits Questionnaire) was used in an online survey to measure gender differences in autistic ( n = 306) and non-autistic adults ( n = 472) without intellectual disability for the first time. Controlling for age and autistic-like traits, an interaction between gender and diagnostic status was found: autistic females demonstrated higher total camouflaging scores than autistic males (partial η 2 = 0.08), but there was no camouflaging gender difference for non-autistic people. Autistic females scored higher than males on two of three Camouflaging Autistic Traits Questionnaire subscales: Masking (partial η 2 = 0.05) and Assimilation (partial η 2 = 0.06), but not on the Compensation subscale. No differences were found between non-autistic males and females on any subscale. No differences were found between non-binary individuals and other genders in either autistic or non-autistic groups, although samples were underpowered. These findings support previous observations of greater camouflaging in autistic females than males and demonstrate for the first time no self-reported gender difference in non-autistic adults.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".