Bibliographic record
Abstract
Alexithymia is a transdiagnostic risk factor for the development and maintenance of psychological symptomatology.Strongly tied to particularities in emotion regulation, recent theorizations consider the difficulties describing and identifying feelings dimensions of alexithymia to influence the appraisal stage of emotional processing.This study aimed at replicating and extending findings from Aaron et al., 2018 by focusing on differentiating alexithymia for positive and negative emotions.Participants (n=125, mean age 45 y.o; 74% women) evaluated their emotional experience after watching videos inducing anger, disgust, sadness, fear, amusement and tenderness.Alexithymia was measured by the Toronto Alexithymia Scale (TAS-20) and Perth Alexithymia Questionnaire (PAQ).No association between alexithymia and positive emotional complexity was observed despite the use of the PAQ assessing alexithymia for positive emotions.Difficulties describing and identifying negative feelings from PAQ were predicted by lower negative granularity.Difficulties describing feelings from both scales were predicted by higher negative dialecticism.These results confirm the association between alexithymia facets and emotional complexity regarding negative emotions only, and emphasize the importance of controlling for negative affect in alexithymia studies.Finally, high alexithymia was characterized by higher reporting of negative affective states compared to positives states following positive induction, which questions the interpretation of dialecticism in alexithymia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.306 | 0.216 |
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".