When Does Diversity Erode Trust? Neighborhood Diversity, Interpersonal Trust and the Mediating Effect of Social Interactions
Bibliographic record
Abstract
This article contributes to the debate about the effects of ethnic diversity on social cohesion, particularly generalized trust. The analysis relies on data from both the ‘Citizenship, Involvement, Democracy’ (CID) survey in the US and the ‘Equality, Security and Community Survey’ (ESCS) in Canada. Our analysis, one of the first controlled cross-national comparisons of small-unit contextual variation, confirms recent findings on the negative effect of neighborhood diversity on white majorities across the two countries. Our most important finding, however, is that not everyone is equally sensitive to context. Individuals who regularly talk with their neighbors are less influenced by the racial and ethnic character of their surroundings than people who lack such social interaction. This finding challenges claims about the negative effects of diversity on trust – at least, it suggests that the negative effects so prevalent in existing research can be mediated by social ties.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".