The contextual effect of trust on perceived support: Evidence from Roma and non‐Roma in East‐Central Europe
Bibliographic record
Abstract
In recent years, trust has been conceptualized as an important source of social capital, setting off cross-disciplinary research on both the benefits and predictors of trust at the individual and contextual level. In this paper, we turn to the individual outcomes of living in a trustful context, and explore the relationship between trust, itself one of the main components of social capital, and social support, seen as one of the most important effects of social capital. In particular, we ask how social capital-and the relationship between trust and social support-functions in the context of unequal societies. We model perceived support as an outcome across three levels, from no support to proximate to distal support, and using a cross-national study of Roma and non-Roma across 12 European countries, we track the relationship between trust and support across both mainstream and marginalized populations. Our findings suggest that living in contexts with more trust has protective effects particularly for members of marginalized groups: the Roma are more likely to have distal support in contexts with higher trust. We conclude that contextual trust helps to broaden the circle of support beyond family and friends; thus, trust can indeed be a synthetic force that binds individuals together in broadened structures of support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".