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Varieties of federalism, institutional legacies, and social policy: Comparing old‐age and unemployment insurance reform in Canada

2012· article· en· W1488248301 on OpenAlexaffabout
Daniel Béland, John Myles

Bibliographic record

VenueInternational Journal of Social Welfare · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of TorontoUniversity of Saskatchewan
Fundersnot available
KeywordsFederalismPoliticsSocial insuranceUnemploymentCorporate governanceHistorical institutionalismDual federalismPolitical sciencePublic administrationPolitical economySociologyEconomicsEconomic growthLawFinance

Abstract

fetched live from OpenAlex

Béland D, Myles J. Varieties of federalism, institutional legacies, and social policy: Comparing old‐age and unemployment insurance reform in Canada With reference to Canada, this article explores the politics of reform affecting two social insurance programs: Employment Insurance (EI) and the Canada Pension Plan (CPP). Comparing these two large, yet institutionally distinct, social insurance schemes underscores how institutional differences in policy legacies and governance among social programs create distinct obstacles and opportunities for reform in federal countries. Drawing on historical institutionalism and emphasizing the types of federalism and decision making specific to EI and CPP, the article explains key political differences between these two programs. Focusing on reforms enacted in the 1990s, the article explores the institutional obstacles and opportunities for policy change in EI and CPP, which offers insight into how these programs could change in the future. We show that different forms of federal governance pointing to the “varieties of federalism” and, more generally, the institutional and territorial logics embedded in these two programs create different obstacles and opportunities for reform.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.032
GPT teacher head0.317
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations33
Published2012
Admission routes2
Has abstractyes

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