Are There Emotional Universals? Evidence from the Native American Language East Cree
Bibliographic record
Abstract
In her study on emotions across languages and cultures, Wierzbicka proposed a set of eleven working hypotheses on emotional universals. We test each of these hypotheses against data newly collected from the Native American language East Cree. Eight of these eleven hypotheses are confirmed, thus giving support to their universality. We offer cross-cultural comparison of anger-like, fear-like and shame-like concepts, and discuss the Cree expression of good and bad feelings, cry and smile, and Cree emotive interjections. Our findings indicate that not all languages commonly use figurative bodily images (‘my heart sank’) or bodily sensations (‘when I heard this, my throat went dry’) to describe cognitively based feelings. The Cree data also cast some doubt on a straightforward universal syntax for combining the primes, as proposed in the current Natural Semantic Metalanguage (NSM) framework. However, we conclude that, for researchers interested in avoiding ethnocentric bias, the NSM approach is on the right track as a tool for cross-cultural, cross-linguistic research on emotions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".