Does language experience and bilingualism shape empathy and emotional intelligence?
Bibliographic record
Abstract
Aims and objectives: Previous research has reported that varied language experiences and language use can play a role in the development of empathy and emotional intelligence (EI). The present study aimed to investigate the association between language experience, bilingualism and personality. Methodology: One hundred and forty-two participants completed a language background questionnaire along with a series of measures to assess empathy and both trait and ability EI. Data and analysis: Measures included the Trait Emotional Intelligence Questionnaire short form (TEIQue), the Toronto Empathy Questionnaire (TEQ), and the Situational Test of Emotional Understanding Brief (STEU-B). Hierarchical regression models, analyses of variance (ANOVAs), and Structural Equation Modelling were used to examine the relationships between language experience, bilingualism, and measures of empathy and EI. Findings: Findings reveal that bilingualism and language experiences did not contribute to empathy and EI. These results question the role that linguistic experience has in shaping empathy and EI. However, a significant difference in empathy and EI scores emerged when comparing participants who processed information in their first (or native) language to those who did not, suggesting that empathy and EI are stronger when processed in the first language. Originality: This study provides a new understanding of the influence of language background on empathy and EI as well as the impact of processing information in a first language. Significance: This study highlights the importance of considering the role that language has in future cross-cultural and cross-linguistic studies. Implications for the use of culturally appropriate measures and future research are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".