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Record W4413134661 · doi:10.1108/aia-04-2025-0032

Autistic people’s perception of social camouflaging: qualitative analysis of a Web forum

2025· article· en· W4413134661 on OpenAlexaff
Justine Larochelle-Guy, Isidoro Martínez Martín, Marie‐Michèle Dufour

Bibliographic record

VenueAdvances in Autism · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsPerceptionPsychologyQualitative researchQualitative analysisSocial psychologySociologySocial science

Abstract

fetched live from OpenAlex

Purpose Camouflaging their autistic characteristics is a common coping strategy among autistic people, often leading to diagnostic delays and negative impacts on mental health. Although existing literature has examined autistic people’s views on camouflaging, it rarely explores these perspectives through natural conversations outside of research settings. The purpose of this study to address this gap and analyze autistic people’s perception of camouflaging based on online discussions between autistic people. Design/methodology/approach This study examined 194 posts from 68 users on a UK-based autism forum, using inductive thematic analysis to identify key aspects of camouflaging. Findings The analysis revealed a predominantly negative view of camouflaging, with most users using it primarily for social integration, especially in work settings. The most reported consequence was exhaustion. Not camouflaging leads not only to improved mental health but also potentially to rejection. Originality/value The findings of this study strengthen existing knowledge about camouflaging by adding new analysis conducted directly on conversations happening organically. Future research should collect more data from natural conversations to validate these results and consider diverse sources of autistic individuals’ perspectives.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.378
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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