The psychological imprint of inequality: Economic inequality shapes achievement and power values in human life
Bibliographic record
Abstract
OBJECTIVE: = 219,697). METHODS: Study 1 examined the relationship between objective economic inequality and values across 77 societies from all five continents (n = 170,525). Study 2 examined the relationship between objective economic inequality and values across 51 regions in the United States (n = 48,559). Study 3 used a two-year longitudinal design to examine the relationship between perceived economic inequality and values (n = 613). RESULTS: Results from multilevel modeling and longitudinal analysis suggested that people who lived in areas with higher economic inequality and who perceived higher economic inequality were more likely to endorse achievement and power values. Moreover, people who perceived higher economic inequality were less likely to endorse benevolence values. These effects were robust in within-country tests (Studies 2 and 3) but not in the cross-country tests (Study 1) when accounting for sociodemographic characteristics. CONCLUSIONS: Our findings suggest that economic inequality may act as an antecedent of self-enhancement values, particularly within countries. In a world of rising economic inequality, this may over time lead to an overemphasis on achievement and power which have been shown to erode social cohesion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".