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Record W4383710633 · doi:10.1177/17456916231178555

What We Can Learn About Emotion by Talking With the Hadza

2023· article· en· W4383710633 on OpenAlexaff
Katie Hoemann, Maria Gendron, Alyssa N. Crittenden, Shani Msafiri Mangola, Endeko S. Endeko, Èvelyne Dussault, Lisa Feldman Barrett, Batja Mesquita

Bibliographic record

VenuePerspectives on Psychological Science · 2023
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsOntario Lung Association
FundersH2020 Marie Skłodowska-Curie ActionsArmy Research Institute for the Behavioral and Social SciencesDivision of Behavioral and Cognitive SciencesNational Science FoundationNational Cancer InstituteEuropean CommissionNational Institute of Mental HealthH2020 European Research CouncilElizabeth R Koch Foundation
KeywordsFeelingNarrativePsychologyContext (archaeology)Meaning (existential)Social psychologyHistory

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.083
GPT teacher head0.412
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2023
Admission routes1
Has abstractyes

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