What We Can Learn About Emotion by Talking With the Hadza
Bibliographic record
Abstract
Emotions are often thought of as internal mental states centering on individuals' subjective feelings and evaluations. This understanding is consistent with studies of emotion narratives, or the descriptions people give for experienced events that they regard as emotions. Yet these studies, and contemporary psychology more generally, often rely on observations of educated Europeans and European Americans, constraining psychological theory and methods. In this article, we present observations from an inductive, qualitative analysis of interviews conducted with the Hadza, a community of small-scale hunter-gatherers in Tanzania, and juxtapose them with a set of interviews conducted with Americans from North Carolina. Although North Carolina event descriptions largely conformed to the assumptions of eurocentric psychological theory, Hadza descriptions foregrounded action and bodily sensations, the physical environment, immediate needs, and the experiences of social others. These observations suggest that subjective feelings and internal mental states may not be the organizing principle of emotion the world around. Qualitative analysis of emotion narratives from outside of a U.S. (and western) cultural context has the potential to uncover additional diversity in meaning-making, offering a descriptive foundation on which to build a more robust and inclusive science of emotion.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".