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Welfare state structures and the structure of welfare state support: Attitudes towards social spending in Canada, 1993–2000

2008· article· en· W2119246429 on OpenAlexaffabout
J. Scott Matthews, Lynda Erickson

Bibliographic record

VenueEuropean Journal of Political Research · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsSimon Fraser UniversityQueen's University
Fundersnot available
KeywordsWelfare stateArgument (complex analysis)WelfareState (computer science)Social WelfareEconomicsSocial classPublic economicsPolitical scienceLawMarket economyComputer sciencePolitics

Abstract

fetched live from OpenAlex

Abstract The comparative welfare state literature contends that different welfare state structures engender different structures of welfare state support. The argument is that social welfare regimes that distribute their benefits selectively tend to produce patterns of support graduated by the likelihood of accessing these selective (or ‘targeted’) social benefits, especially as indexed by social class. Where benefits are universally distributed, by contrast, support is expected to be more consensual and to cut across class and related cleavages. This article empirically tests this ‘interest‐based’ account and extends it by adding a ‘values‐based’ component. The authors find that the impact of both interests and values – specifically, orientations toward the capitalist system – on welfare state support is conditional on welfare state structures. It is argued that these results help to resolve a paradox in the comparative welfare state literature: strong evidence for differentiation in social welfare support by program type, but weak evidence for differentiation in class effects by program type. Data for the analysis come from the Canadian Election Studies of 1993, 1997 and 2000.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.389
Teacher spread0.310 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2008
Admission routes2
Has abstractyes

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