Levels of Emotional Awareness Scale scores as predictors of interactive behavior: A validation study.
Bibliographic record
Abstract
Emotional awareness is a fundamental emotional ability that has been associated with increased social adaptation, social functioning, and health. Lane and Schwartz (1987) conceptualized the Levels of Emotional Awareness (LEA) model which postulated emotional awareness as a cognitive ability that undergoes five different developmental levels of structural alteration. In order to measure differences in emotional awareness, Lane and Schwartz (1992) subsequently developed the Levels of Emotional Awareness Scale (LEAS). The present study attempted to validate this scale by demonstrating correspondence between emotional awareness and emotional components of social interactive behavior. Based on the literature depicting associations between emotional awareness and explicit behavior in social contexts it was hypothesized that individuals scoring higher on the LEAS would receive higher ratings on such behavioral dimensions during social interaction. It was also hypothesized that females would receive higher ratings than males on the behavioral dimensions being measured as females have previously been found to score higher than males on the LEAS. Eleven psychology graduate students rated the social interactive behaviors of video encoders participating in social interactive tasks. Video encoders with higher LEAS scores received higher ratings on the behavioral dimensions of social deftness and impulse control. The female video encoders however did not receive higher ratings than males on behaviors correlated with emotional awareness which was contrary to previous research findings. The results confirm that level of emotional awareness does correspond to emotional behavior in interpersonal interaction. This confirmation adds increased validity to the LEAS as an emotion assessment tool.--P.ii.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".