Eliciting Expressions of Emotion: An Exploratory Analysis of Alexithymia in Adults with Autism Utilising the APRQ
Bibliographic record
Abstract
This study examined alternative methods for detecting alexithymia to the Toronto Alexithymia Scale-20 (TAS-20) by comparing the emotional linguistic performance of ASD and NT samples (n = 32 in each) on the Alexithymia Provoked Responses Questionnaire (APRQ). We utilised both the LIWC and tidytext approaches to linguistic analysis. The results indicate the ASD sample used significantly fewer affective words in response to emotionally stimulating scenarios and had less emotional granularity. Affective word use was correlated with ASD symptomatology but not with TAS-20 scores, suggesting that some elements of alexithymia are not well detected by the TAS-20 alone. The APRQ, in combination with the tidytext package, offers significant potential for sophisticated exploration of emotional expression in ASD.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".