Factor structure and correlates of the Mandarin version of the Managing the Emotions of Others (MEOS) scale
Bibliographic record
Abstract
The English-language version of the Managing the Emotions of Others (MEOS) scale has been found to have a six-factor structure. This includes two pairs (Enhance, Divert and Worsen, Inauthentic) that respectively describe prosocial and non-prosocial interpersonal emotion management, together with an emotional concealment factor (Conceal) and a factor assessing poor self-rated emotional skills. A Mandarin translation of the MEOS was completed by 277 Chinese student participants. Factor analysis indicated a four-factor structure comprising a merged Enhance/Divert factor, together with Worsen, Inauthentic and Conceal factors. The emergence of a different factor structure compared to Western samples may be related to culture-dependent attitudes to emotional expression. The associations of the MEOS factors with Five-Factor model personality, the Dark Triad and trait emotional intelligence (EI) were examined; these were similar to but generally weaker than those found for the English-language version.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".