Commentary: ‘Camouflaging’ in autistic people – reflection on Fombonne (2020)
Bibliographic record
Abstract
Fombonne's (2020) editorial is a thought-provoking appraisal of the literature on 'camouflaging', whereby some autistic people mask or compensate for their autistic characteristics as an attempt to fit in and to cope with disabilities under neurotypical social norms. Fombonne (2020) highlights three issues of contention: (a) construct validity and measurement of camouflaging; (b) camouflaging as a reason for late autism diagnosis in adolescence/adulthood; and (c) camouflaging as a feature of the 'female autism phenotype'. Here, we argue that (a) establishing construct validity and measurement of different aspects of camouflaging is warranted; (b) subjective experiences are important for the differential diagnosis of autism in adolescence/adulthood; and (c) camouflaging is not necessarily a feature of autism in female individuals - nevertheless, taking into account sex and gender influences in development is crucial to understand behavioural manifestations of autism. Future research and clinical directions should involve clarification of associated constructs and measurements, demography, mechanisms, impact (including harms and benefits) and tailored support.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.008 |
| 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".