How sources of purpose predict meaning in life, happiness, and psychological richness, across cultures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Past research has shown many benefits of having a sense of purpose. However, less is known about the specific kinds of purpose that guide individual’s lives. Using over 2000 open-ended purposes supplied by American adults, we identified 16 common kinds of purposes. We then collected data (n = 1048) from participants in the United States, Poland, Japan and India in order to explore the cross-cultural generalizability of these purposes and to see how well they predicted three forms of the good life; meaningful, happy, and psychologically rich. Overall, the 16 different sources of purpose were commonly endorsed and to a strikingly similar degree across cultures. These different sources of purpose also similarly predicted forms of the good life across nations, with some cultural variation. Taken together, our findings suggest that some kinds of purposes are especially relevant in predicting people’s well-being and that these relations are largely robust across cultures.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it