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Record W2732145579

A European Unemployment Benefits Scheme: Lessons from Canada

2017· article· en· W2732145579 on OpenAlexaboutno aff
Donna E. Wood

Bibliographic record

VenueCEPS Papers · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentSolidarityIncome SupportLegitimacySocial insuranceEconomicsPolitical scienceLabour economicsEconomic growthPoliticsLawMarket economy
DOInot available

Abstract

fetched live from OpenAlex

In many federal political systems, responsibility for unemployment has a multi-tiered architecture, with competence for key elements such as unemployment insurance, social assistance, and the public employment service, dispersed across different orders of government. This CEPS Working Document tells the story of the long transformation of unemployment insurance into a federal responsibility in Canada, and seeks to identify lessons from Canada’s experience that might help Europeans consider the potential of an EU-wide unemployment benefits scheme. Most European scholars look to the United States for transferable ideas; this author argues that Canada is a more salient comparator, given that it has similar institutional features to the EU, and has successfully managed a pan-Canadian unemployment insurance benefits scheme for over 75 years. Lessons for the EU from Canada include the place of a centrally managed unemployment insurance programme in a monetary union, and insights with respect to stabilisation, labour mobility, redistribution, social solidarity, legitimacy, and institutional moral hazard.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.166
Threshold uncertainty score0.967

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0180.005
Scholarly communication0.0100.003
Open science0.0020.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.060
GPT teacher head0.337
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

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