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Record W2344350806 · doi:10.1080/14616696.2016.1172717

Welfare regimes and social cohesion regimes: do they express the same values?

2016· article· en· W2344350806 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Societies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCohesion (chemistry)TypologyWelfare stateSolidarityWelfareMultidimensional scalingDialecticPositive economicsSociologyEconomic systemEconomicsSocial psychologyPolitical sciencePsychologyEpistemologyLawMathematicsStatistics

Abstract

fetched live from OpenAlex

Welfare regime types are classified according to the role played by three main institutions, namely the market, the state and the family. They can be reinterpreted as systems of exchanges for providing resources based on the main principles of liberty, equality and solidarity. Depending on the different possible dialectical relations between these three principles, they lead to different social cohesion regimes. This paper is the first attempt to empirically test this hypothesis at a European level by elaborating a measure of social cohesion based on values and creating a typology of social cohesion regimes. In comparison to welfare regimes, it invites to go one step further by considering the articulation between the three main principles and proposes a more precise classification for countries. The results invite further research on the links between welfare and social cohesion regimes. The analysis is based on data from the 2008 European Values Study (EVS) in 43 countries using principal component analysis, multidimensional scaling and cluster analyses.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
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 head0.033
GPT teacher head0.293
Teacher spread0.260 · 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