Unpacking the hedonic paradox: A dynamic analysis of the relationships between financial capital, social capital and life satisfaction
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".