Measuring Clarity of and Attention to Emotions
Bibliographic record
Abstract
Previous research has found that understanding one's emotions and attending to them are 2 dimensions of emotional awareness. In this research, we examined whether improved subscales for measuring clarity of and attention to emotions could be developed by selecting the best items from 2 frequently used measures of emotional awareness. Using multidimensional scaling and confirmatory factor analysis, we analyzed the Toronto Alexithymia Scale–20 (Bagby, Parker, & Taylor, 1994 Bagby, R. M., Parker, J. D. A. and Taylor, G. J. 1994. The twenty-item Toronto Alexithymia Scale: I. Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research, 38: 23–32. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) and the Trait Meta-Mood Scale (Salovey, Mayer, Goldman, Turvey, & Palfai, 1995 Salovey, P., Mayer, J. D., Goldman, S. L., Turvey, C. and Palfai, T. P. 1995. “Emotional attention, clarity, and repair: Exploring emotional intelligence using the trait meta-mood scale”. In Emotion, disclosure, & health, Edited by: Pennebaker, J. 125–154. Washington, DC: American Psychological Association. [Crossref] , [Google Scholar]) data from 867 college students. Results supported distinct clarity and attention constructs. New subscales were internally consistent and fared as well as or better than previous versions in terms of internal consistency and convergent validity.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".