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Record W2171847733 · doi:10.1037/a0031578

Prosocial Spending and Well-Being: Cross-Cultural Evidence for a Psychological Universal

2010· article· en· W2171847733 on OpenAlexaffabout
Lara B. Aknin, Christopher Barrington‐Leigh, Elizabeth W. Dunn, John F. Helliwell, Robert Biswas‐Diener, Imelda Kemeza, Paul Nyende, Claire E. Ashton‐James, Michael I. Norton

Bibliographic record

VenueJournal of Personality and Social Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProsocial behaviorHappinessAltruism (biology)PsychologySocial psychologyCausality (physics)World Values SurveyEconomics

Abstract

fetched live from OpenAlex

This research provides the first support for a possible psychological universal: Human beings around the world derive emotional benefits from using their financial resources to help others (prosocial spending). In Study 1, survey data from 136 countries were examined and showed that prosocial spending is associated with greater happiness around the world, in poor and rich countries alike. To test for causality, in Studies 2a and 2b, we used experimental methodology, demonstrating that recalling a past instance of prosocial spending has a causal impact on happiness across countries that differ greatly in terms of wealth (Canada, Uganda, and India). Finally, in Study 3, participants in Canada and South Africa randomly assigned to buy items for charity reported higher levels of positive affect than participants assigned to buy the same items for themselves, even when this prosocial spending did not provide an opportunity to build or strengthen social ties. Our findings suggest that the reward experienced from helping others may be deeply ingrained in human nature, emerging in diverse cultural and economic contexts.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.114
GPT teacher head0.472
Teacher spread0.358 · 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

Citations62
Published2010
Admission routes2
Has abstractyes

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