Do Incremental Theories of Well-Being and Other Well-Being Beliefs Predict Prosocial Spending?
Bibliographic record
Abstract
Research has revealed that prosocial spending is associated with greater well-being than is personal spending. Given this link, well-being beliefs may predict the degree to which people endorse prosocial spending relative to personal spending. In Study 1, it was predicted that incremental mindsets toward well-being and eudaimonic well-being beliefs would be associated with prosocial spending and autonomous reasons for prosocial spending. Results showed that undergraduate students (N = 288) with greater eudaimonic and lower hedonic well-being beliefs demonstrated an increased preference for prosocial spending and autonomous reasons for prosocial spending, controlling for socially desirable responding. In Study 2, it was predicted that the cultivation of an incremental mindset and the delivery of knowledge regarding the benefits of prosocial spending would interact to enhance the intention to engage in prosocial spending. Undergraduate student participants were randomly assigned to a mindset intervention, a benefits-of-prosocial-spending intervention, both interventions, or neither intervention. Results revealed that undergraduate students (N = 84) assigned to the incremental mindset of well-being condition demonstrated a greater endorsement of prosocial spending. The implications of the current research will be discussed, including mechanisms by which well-being beliefs are or are not associated with prosocial spending. Department: Psychology Faculty Mentor: Dr. Andrew Howell
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".