Income inequality, trust and homicide in 33 countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it