Testing the Link Between Empathy and Lay Theories of Happiness
Bibliographic record
Abstract
Happiness is a topic that ignites both considerable interest and considerable disagreement. Thus far, however, there has been little attempt to characterize people's lay theories about happiness or explore their consequences. We examined whether individual differences in lay theories of happiness would predict empathy. In Studies 1a and 1b, we validated the Lay Theories of Happiness Scale (LTHS), which includes three dimensions: flexibility, controllability, and locus. In Study 2, higher dispositional empathy was predicted by the belief that happiness is flexible, controllable, and internal. In Studies 3 and 4, higher empathy toward a specific target was predicted by the belief that happiness is flexible, uncontrollable, and external. In conjunction, Studies 2, 3, and 4 provide evidence that trait and state empathy are separable and can have opposing relationships with people's lay theories. Overall, these findings highlight generalized beliefs that may guide empathic reactions to the unhappiness of others.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".