Open Access The reliability and validity of the Japanese version of the Levels of Emotional Awareness Scale (LEAS-J)
Bibliographic record
Abstract
Background: The Levels of Emotional Awareness Scale (LEAS) was developed to assess five levels of emotional awareness: bodily sensations, action tendencies, single emotions, blends of emotion, and combinations of blends. It is a paper and pencil performance questionnaire that presents 20 emotion-evoking scenes. We developed a Japanese version of the LEAS (LEAS-J), and its reliability and validity were examined. Methods: The LEAS-J level was independently assessed by two researchers who scored each response according to the LEAS scoring manual. High inter-rater reliability and internal consistency were obtained for the LEAS-J. Measures were socioeconomic status, LEAS-J, Toronto Alexithymia Scale-20 (TAS-20), Interpersonal Reactivity Index (IRI), and NEO Five-Factor Inventory (NEO-FFI). TAS-20, IRI and NEO-FFI were the measures used to explore the construct validity of LEAS-J, as it was predicted that higher scores on the LEAS-J would be related to fewer alexithymic features, greater empathetic ability, and a greater sense of cooperation with others. Questionnaires were completed by 344 university students. Results: The criterion-referenced validity was determined: a significant negative relationship was found with the externally-oriented thinking scores of TAS-20, and positive relationships were found with fantasy, perspective taking, and empathic concern on IRI and with extraversion, openness to experience, and agreeableness on NEO-FFI.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.106 | 0.013 |
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".