Cousins or conjoined twins: how different are meaning and happiness in everyday life?
Bibliographic record
Abstract
Are experiences that bring meaning different from experiences that bring happiness? If so, do people seek out different experiences in pursuing meaning versus happiness? In an adversarial collaboration, we conducted three preregistered experiments (total N = 879) to address these questions. We asked participants to describe an experience from the past month (Study 1) or past day (Study 2) that had provided them with either happiness, meaning, happiness without meaning, or meaning without happiness. Experiences that were happy but not meaningful differed substantially from those that were meaningful but not happy. However, experiences that provided happiness showed only small differences from those that provided meaning. In Study 3, to examine whether people seek out different experiences in pursuing happiness versus meaning, we instructed participants to choose an activity over the weekend that would provide them with happiness, meaning, happiness without meaning, or meaning without happiness. Again, experiences differed substantially when people pursued happiness without meaning or meaning without happiness, but these differences disappeared when people were simply told to pursue happiness or meaning. Our findings suggest that happiness and meaning are linked to distinct sets of thoughts, feelings and behaviors, but the differences between them are small in everyday life.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".