Bibliographic record
Abstract
BACKGROUND: Theories of why income inequality correlates with violence suggest that inequality erodes social capital and trust, or inhibits investment into public services and infrastructure. Past research sensed the importance of these causal paths but few have examined them using tests of statistical mediation. METHODS: We explored links between income inequality and rates of homicide in 33 countries and then tested whether this association is mediated by an indicator of social capital (interpersonal trust) or by public spending on health and education. Survey data on trust were collected from 48 641 adults and matched to country data on per capita income, income inequality, public expenditures on health and education and rate of homicides. RESULTS: Between countries, income inequality correlated with trust (r = -0.64) and homicide (r = 0.80) but not with public expenditures. Trust also correlated with homicides (r = -0.58) and partly mediated the association between income inequality and homicide, whilst public expenditures did not. Multilevel analysis showed that income inequality related to less trust after differences in per capita income and sample characteristics were taken into account. CONCLUSION: Results were consistent with psychosocial explanations of links between income inequality and homicide; however, the causal relationship between inequality, trust and homicide remains unclear given the cross-sectional design of this study. Societies with large income differences and low levels of trust may lack the social capacity to create safe communities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".