Bibliographic record
Abstract
The aim of this dissertation was the assessment of possible correlations between alexithymia and the recognition of emotions in a sample of healthy subjects. Self-rated alexithymia (according to the altered factorial structure of the German version of the Toronto Alexithymia Scale – TAS-20) was correlated with objectively measured emotion recognition performance. Emotions were presented by facial expressions (Facially Expressed Emotion Labeling - FEEL) and text-based scenic descriptions of social interactions (Levels of Emotional Awareness Scale - LEAS). Objectively assessed emotion recognition (FEEL and LEAS) correlated positively with the importance of emotional introspection (TAS-20). External thinking (TAS-20) correlated negative with performance in the Levels of Emotional Awareness Scale. There was no correlation between the emotional performance tests (FEEL and LEAS) and the core of alexithymia, difficulties in identifying and describing emotions (TAS-20). Alexithymia (TAS-20 total score) is related with mental strain (GSI SCL-90-R). Another focus was on gender-specific differences in emotion recognition performance. Women performed significantly better in the Levels of Emotional Awareness Scale, in LEAS Score Self and especially in LEAS Score Other. Emotional introspection (TAS-20) was more important for the female participants and they showed less external thinking (TAS-20). In this study there was no significant correlation between self-rated alexithymia (TAS-20 total score) and deficits in the recognition of emotions shown by facial expression (FEEL).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".