To feel and talk in a language of conflict: distinct emotional experience and expression of bilinguals among disadvantaged minority members
Bibliographic record
Abstract
Research conducted on emotionality in bilinguals suggests that language use modulates emotional expression. The current study examines bilingual disadvantaged minority members’ emotional experience and expression as shaped by the group relations in a conflict area. We hypothesised that, in general, greater emotionality will be found in one’s native language. Moreover, since the second language is imposed and acquired in a negative context, there may be differential effects on negative and positive language. A novel ecological paradigm was used: Twenty-eight Palestinian citizens of Israel were videotaped while recounting emotional stories in both Arabic (L1) and Hebrew (L2), resulting in 212 videos. Two studies followed: In Study 1 we compared participants’ emotional ratings (1a) and analyzed the content of emotional expression (1b). In Study 2, American participants rated emotional expressiveness. In Study 1, an interaction effect was found between language and valence, with less positive emotions and expressions in L2. In Study 2, a general difference in expressiveness was found in favour of L1. These studies show an effect of power disparities on the emotional load of the second language, thus highlighting the emotional costs of using a second language acquired in a conflict area.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".