Demographic Differences in Camouflaging Autistic Traits Questionnaire and the Toronto Empathy Questionnaire
Bibliographic record
Abstract
Empathy and social masking are traits related to autism spectrum disorder (ASD). Social masking, the act of camouflaging socially to appear closer to the social norm, is often utilized to conceal autistic traits, such that individuals with ASD mask more frequently than neurotypical individuals (Hull et al., 2017). However, neurotypical adults also use masking and camouflaging behaviors in routine social interactions, including actively attempting to mirror others’ moods, reflecting vocabulary and syntax, or matching facial expressions to respond appropriately (Pryke-Hobbes et al. 2023). Additionally, empathy is related to ASD traits; although, the findings are often mixed. Originally, it was thought that people with autism lacked the level of empathy seen in neurotypical populations (Charman et al., 1997). However, this conclusion resulted from poor definitions of empathy and unreliable testing (Fletcher-Watson & Bird, 2020). The increased interest and research on empathy and masking have led to new assessments, such as the Toronto Empathy Questionnaire (TEQ) and the Camouflaging Autistic Traits Questionnaire (CAT-Q). The current study compares the demographic characteristics (i.e., age and gender) of a subclinical college student sample on the TEQ and the CAT-Q. Social masking plateaus in early adulthood; however, scores diverge in later adulthood for those with autism symptoms, showing that those with autism traits mask at higher rates (Remnélius & Bölte, 2023). Additionally, the TEQ has shown that older adults have significantly higher empathy scores than younger adults (Gould & Gautreau, 2014). Research on gender suggest no significant differences in CAT-Q total or subscale scores between non-autistic males and females (Hull et al., 2019a). However, in autistic populations, females score significantly higher than males in both the total and subscale scores.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".