Justice beliefs for self and others: Associations with positive and negative affectivity in African Americans and White Americans
Bibliographic record
Abstract
Prior research has shown that a belief in personal justice (i.e., justice for self) is associated with better health and well-being, whereas a belief in justice more generally (i.e., justice for others) is unrelated. However, an emerging perspective is that racial differences may overlay the relationships between multidimensional beliefs about justice and indices of well-being. This includes that well-being among African Americans may be additionally supported by rejecting rather than endorsing some forms of believing in justice. In the present study, we consider racial similarities and differences in the links between beliefs about justice for self and others and emotional well-being. African Americans (N = 117) and White Americans (N = 188) completed measures of beliefs about justice for self and others, and also measures of dispositional tendencies towards experiencing positive and negative emotion (i.e., positive and negative affectivity). In both groups, beliefs about justice for the self were associated with greater positive affect and reduced negative affect. However, beliefs about justice for others were additionally associated with greater negative affect only among African Americans. The link between justice for others and negative affect among African Americans was not attributable to measurement or mean differences in justice beliefs across racial groups, or to socioeconomic differences. Results align with an emerging perspective that simultaneously endorsing and rejecting justice beliefs may be vital to preserving well-being for some racial minorities.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".