Adaptation of Lane's and Schwartz's Levels of Emotional Awareness Scale
Bibliographic record
Abstract
The paper presents adaptation of Levels of Emotional Awareness Scale (LEAS) created originally by Lane and Schwartz. According to their theory emotional awareness is a type of cognitive processing which undergoes five levels of structural transformation. LEAS is a written, projective instrument that asks subjects to describe her or his anticipated emotions and those of another person in each of 20 scenes described in 2 to 4 sentences. Scoring criteria allow to evaluate the degree of differentiation and integration of the words denoting emotions and the level of emotional awareness of the subject. Adaptation of the scale was conducted with a group of 113 subjects (aged – 19, equal number women and men) and group of 56 women (aged: 35 to 40). In order to establish reliability of the instrument three methods were used: 1) split-half reliability, 2) internal reliability and 3) test – retest reliability. All results obtained are showing high reliability of LEAS – PL. Validity of the scale was evaluated by showing its positive correlation with ALEX–40 (a questionnaire used for a measurement of alexithymia) and negative correlation with two verbal test of WAIS – R PL: Vocabulary and Comprehension. Construct validity was checked by group differences (women and men).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".