Developing an international scoring system for a consensus-based social cognition measure: MSCEIT-managing emotions
Bibliographic record
Abstract
BACKGROUND: Measures of social cognition are increasingly being applied to psychopathology, including studies of schizophrenia and other psychotic disorders. Tests of social cognition present unique challenges for international adaptations. The Mayer-Salovey-Caruso Emotional Intelligence Test, Managing Emotions Branch (MSCEIT-ME) is a commonly-used social cognition test that involves the evaluation of social scenarios presented in vignettes. METHOD: This paper presents evaluations of translations of this test in six different languages based on representative samples from the relevant countries. The goal was to identify items from the MSCEIT-ME that show different response patterns across countries using indices of discrepancy and content validity criteria. An international version of the MSCEIT-ME scoring was developed that excludes items that showed undesirable properties across countries. RESULTS: We then confirmed that this new version had better performance (i.e. less discrepancy across regions) in international samples than the version based on the original norms. Additionally, it provides scores that are comparable to ratings based on local norms. CONCLUSIONS: This paper shows that it is possible to adapt complex social cognitive tasks so they can provide valid data across different cultural contexts.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".