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Record W2579892600 · doi:10.1177/0020715216688934

The welfare state and social capital in Europe: Reassessing a complex relationship

2017· article· en· W2579892600 on OpenAlexvenueno aff
Emanuele Ferragina

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsGenerosityWelfare stateSocial capitalEconomicsWelfareExplanatory powerSociologyPolitical scienceMarket economyLawSocial sciencePolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.181
GPT teacher head0.474
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2017
Admission routes1
Has abstractyes

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