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Record W3047059449 · doi:10.3138/cpp.2020-007

Partisan Priorities under Fiscal Constraints in Canadian Provinces

2020· article· en· W3047059449 on OpenAlexaffvenueabout
Olivier Jacques

Bibliographic record

VenueCanadian Public Policy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsGovernment (linguistics)Fiscal federalismHealth careFiscal policyEconomicsIdeologyPublic economicsDemographic economicsPolitical scienceEconomic growthPoliticsMacroeconomicsDecentralization

Abstract

fetched live from OpenAlex

Fiscal federalism, aging, and rising health care costs are constraining Canadian provinces’ fiscal room to maneuver. Can provincial government partisanship influence policy choices when governments face fiscal pressures? This article studies the impact of fiscal pressures on provincial governments’ expenditure priorities, conditional on government partisanship. It argues that policy feedback and the preferences of the governing party’s core constituency determine expenditure priorities. Using a compositional dependent-variable analysis, it models budget policy choices in Canadian provinces from 1981 to 2018. When provinces undergo different types of fiscal pressures, the proportion of health care expenditures increases, while “other” government expenditures, which are the programs that are not classified as health care, education, or social spending, are retrenched. While governments’ ideology does not modify the crowding out of “other” expenditures by health care, left-wing governments prioritize social expenditures, while right-wing governments retrench them.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.608

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.055
GPT teacher head0.319
Teacher spread0.264 · 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 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

Citations19
Published2020
Admission routes3
Has abstractyes

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