Are concepts of achievement-related emotions universal across cultures? A semantic profiling approach
Bibliographic record
Abstract
Verifying that conceptualisations of emotions are consistent across languages and cultures is a critical precondition for meaningful cross-cultural research on emotional experience. For achievement-related emotions tied to successes or failures, such evidence is virtually non-existent. To address this gap, we compared Canadian, German, Colombian, and Chinese university students’ (NTotal = 126) perceptions of affective, cognitive, motivational, physiological, and expressive characteristics of 16 achievement-related emotions using a psycholinguistic tool for profiling emotion concepts (Achievement Emotions CoreGRID). Cross-cultural similarity of emotion concepts quantified through double-entry intraclass correlations was generally high, and highest for their affective, cognitive, and motivational components. However, results also point to cultural variation, particularly for physiological and expressive components. Variation in perceived physiological characteristics was most pronounced for boredom, and for comparisons of Canada, Germany, and Colombia with China. Implications for theoretical propositions of universality of emotion concepts and future research on achievement-related emotions are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".