Belief in a just world and well-being: A daily diary perspective
Bibliographic record
Abstract
A large body of research has examined the relationship between belief in a just world (BJW) and well-being. However, this research, and work on BJW more broadly, has predominantly employed experimental and cross-sectional methods, which may not adequately capture how BJW functions in daily life. To help address this, we considered how two forms of BJW—believing the world is just for the self (personal-BJW) and just for others (general-BJW)—relate to various aspects of well-being between persons in a cross-sectional study ( N = 512) and, critically, within persons in a 2-week naturalistic daily diary study ( N = 132; 1439 daily reports). Results revealed that both personal- and general-BJW varied between- and within-individuals. Moreover, personal-BJW was not only more robustly related to greater well-being than general-BJW at the between-person level, consistent with prior work, but also at the within-person level. Overall, our diary findings suggest that BJW fluctuates in daily life and that these fluctuations covary positively with well-being. • Assessed two forms of belief in a just world (BJW): personal-BJW and general-BJW • Examined how both forms of BJW were related to well-being. • Utilized between-person (cross-sectional) and within-person (daily diary) approaches • Personal-BJW was a more robust positive predictor of well-being than general-BJW.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".