Self-categorization and autism: Exploring the relationship between autistic traits and group homogeneity.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Integrated Self-Categorization model of Autism (ISCA; Bertschy et al., 2019; Skorich & Haslam, 2021) argues that the theory of mind differences seen in autism arises from Enhanced Perceptual Functioning/Weak Central Coherence, via a dysfunctional self-categorization mechanism. The ISCA model also makes the novel prediction that phenomena that arise from self-categorization should also be affected in autistic people. In this article, we report three studies exploring this prediction in the context of one such phenomenon: Group homogeneity. We first measure participants' autistic traits, then ask them to make homogeneity judgments of their ingroup alone or their outgroup alone (in Study 1, and in the Alone conditions of Studies 2a and 2b); or of their ingroup in comparison to their outgroup or their outgroup in comparison to their ingroup (in the Compare conditions of Studies 2a and 2b). As predicted, we find that: the degree of autistic traits negatively predicts ratings of group homogeneity; this relationship is mediated by social identification/self-categorization; and typical comparison-related homogeneity effects are strengthened at higher relative to lower levels of autistic traits. These studies provide convergent evidence for the ISCA model and suggest important avenues for well-being and social skills interventions for autistic people. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it