Introduction to a Culturally Sensitive Measure of Well-Being: Combining Life Satisfaction and Interdependent Happiness Across 49 Different Cultures
Bibliographic record
Abstract
Abstract How can one conclude that well-being is higher in country A than country B, when well-being is being measured according to the way people in country A think about well-being? We address this issue by proposing a new culturally sensitive method to comparing societal levels of well-being. We support our reasoning with data on life satisfaction and interdependent happiness focusing on individual and family, collected mostly from students, across forty-nine countries. We demonstrate that the relative idealization of the two types of well-being varies across cultural contexts and are associated with culturally different models of selfhood. Furthermore, we show that rankings of societal well-being based on life satisfaction tend to underestimate the contribution from interdependent happiness. We introduce a new culturally sensitive method for calculating societal well-being, and examine its construct validity by testing for associations with the experience of emotions and with individualism-collectivism. This new culturally sensitive approach represents a slight, yet important improvement in measuring well-being.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".