The welfare state and social capital in Europe: Reassessing a complex relationship
Bibliographic record
Abstract
The article investigates the relationship between the welfare state and social capital in Europe during the 1990s and the 2000s using structural equation modelling (SEM). By formulating and testing the hypothesis that welfare state generosity and welfare state size have different effects on social capital, we reassess the explanatory power of the main theories in the field and the findings of previous empirical work. We strongly support the contention of institutional theory that there is a positive association between high degrees of welfare state generosity and social capital. Moreover, we partially confirm the concern of neoclassical and communitarian theories for the negative correlation between large-size welfare states and social capital. The positive relationship between welfare state generosity and social capital is much stronger than the negative association observed with welfare state size. Finally, we interpret the considerable cross-country variation using welfare regime theory and several country cases. We illuminate different mechanisms linking welfare state development and social capital creation, discussing the Danish and Dutch third sector experiences and pointing to Sweden as an exceptional case of decline. Furthermore, we highlight the importance of regional variation in Belgium, Germany and Italy and complement the analysis also briefly discussing the Austrian, French, Irish and British cases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".