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Unpacking the hedonic paradox: A dynamic analysis of the relationships between financial capital, social capital and life satisfaction

2011· article· en· W1816408537 on OpenAlexaff
Ilka H. Gleibs, Thomas A. Morton, Anna Rabinovich, S. Alexander Haslam, John F. Helliwell

Bibliographic record

VenueBritish Journal of Social Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of British Columbia
FundersEconomic and Social Research Council
KeywordsHappinessSocial capitalLife satisfactionSocial psychologyContext (archaeology)PsychologyEconomicsPerspective (graphical)Priming (agriculture)Positive economicsSociologySocial science

Abstract

fetched live from OpenAlex

Does money buy happiness? Or is happiness derived from looking outwards towards our social networks? Many researchers have answered these questions by exploring whether the best predictor of well-being is either economic or social (or some fixed combination of the two). This paper argues for a dynamic perspective on the capacity for economic and social factors to predict well-being. In two studies, we show that both money (individual income) and community (social capital) can be the basis for individual happiness. However, the relative influence of each factor depends on the context within which happiness is considered, and how this shapes the way people define the self. Study 1 primes either money or community in the laboratory and demonstrates that such priming shifts individual values (so that they are economic vs. communal) and determines the extent to which income is more (vs. less) predictive of life satisfaction than social relations. Study 2 looks at these same priming processes in the external world (with people travelling to vs. from work). Both studies show that while money can become the basis of happiness when the self is defined in economic terms, the role of community relations in predicting happiness is more stable across 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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.325
Teacher spread0.271 · 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

Citations21
Published2011
Admission routes1
Has abstractyes

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