A triple trust penalty? The majority-minority gap in subjective wellbeing
Bibliographic record
Abstract
This study introduces a social capital perspective to the majority-minority gap in wellbeing. We explore the role of social trust and test specifically whether racial and ethnic minorities may experience a triple trust penalty. First is a level penalty, where minorities exhibit lower levels of trust, potentially adversely affecting their wellbeing. Second, there may be a return penalty, where minorities may experience a diminished return from being trustful. Third, there may be a protection penalty, where minorities experience reduced benefits from residing in a high-trust context. Our empirical analyses are based on data from multiple waves of the European Social Survey (Round 4–10, 2008–2020) with over 300,000 individuals from 38 European countries. Our analyses indicate support for the level penalty, but we found no evidence for the return or protection penalties. Specifically, we show that racial and ethnic minorities’ lower levels of trust can have harmful impacts on their happiness and life satisfaction. However, an increase in trust yields greater wellbeing among racial and ethnic minorities, and residing in a high-trust context also appears to have a more substantial impact on the well-being of racial and ethnic minorities as compared to their counterparts. The results suggest that promoting trust can effectively narrow the wellbeing gap among various racial groups. • This study introduces a social capital perspective to the majority-minority gap in wellbeing. • Socioeconomic inequality and discrimination factors can account for a large share but not all of the gap in wellbeing. • Racial and ethnic minorities' lower levels of trust can have harmful impacts on their wellbeing. • An increase in trust yields greater wellbeing among racial and ethnic minorities. • Residing in a high-trust context has a more substantial impact on racial and ethnic minorities' wellbeing.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".