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Record W2895027516 · doi:10.1080/21699763.2018.1526698

Federalism and the politics of the Canada and Quebec Pension Plans

2018· article· en· W2895027516 on OpenAlexfundaboutno aff
Daniel Béland, R. Kent Weaver

Bibliographic record

VenueJournal of International and Comparative Social Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
FundersCanada Research ChairsGeorgetown University
KeywordsFederalismPoliticsScope (computer science)PensionPublic administrationPolitical sciencePlan (archaeology)BenchmarkingPublic policyPension planEconomicsGeographyLawManagement

Abstract

fetched live from OpenAlex

Abstract Drawing on the literature on federalism and public policy, the present article explores the recent politics of two highly-similar and closely integrated Canadian public pension programs created in the mid-1960s: the Canada Pension Plan (CPP) and the Quebec Pension Plan (QPP). This article argues that the parallel evolution of CPP/QPP can be understood by examining how the unique jurisdictional arrangements for the CPP/QPP interacted with other factors to generate by these linked programs have led to the emergence of specific federalism policy dynamics, while muting or foreclosing other potential policy dynamics. As shown, governments have engaged in a process of ‘collusive benchmarking’ that has limited the scope of the available policy options. Differing demographic trends in Quebec and the ‘Rest of Canada’ have strained but also reinforced this policymaking dynamic in recent years. Simultaneously, intergovernmental race to the top dynamics have facilitated the recent push for both CPP and, later, QPP expansion.

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)0.003
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.007
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0020.003
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 head0.047
GPT teacher head0.358
Teacher spread0.311 · 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
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

Citations30
Published2018
Admission routes2
Has abstractyes

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