Measuring Clarity of and Attention to Emotions
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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