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Record W3092199794 · doi:10.1080/00036846.2020.1853670

Elections, economic outcomes and policy choices in Canada: 1870 – 2015

2020· article· en· W3092199794 on OpenAlexaffabout
J. Stephen Ferris, Marcel Voia

Bibliographic record

VenueApplied Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsCarleton University
Fundersnot available
KeywordsEconomicsFiscal policyBusiness cycleVolatility (finance)UnemploymentPer capitaMonetary policyMonetary economicsPoliticsMacroeconomicsEconometricsPolitical science

Abstract

fetched live from OpenAlex

In this paper we examine the relationship between economic and electoral outcomes in Canada since Confederation (1867) and the role that economic policy has played in influencing this relationship. The results are consistent with voter concern for the overall performance of the economy in the incumbent’s governing term – the average growth rate of per capita GDP and average unemployment rate – while rejecting the presence of a political business cycle. Evidence of an effect of performance on the stability of the political party system (as measured by party vote volatility) is even stronger. The results also suggest that economic policy has only indirect effects on election outcomes, the most direct being the destabilizing influence of tax increases on party structure. The data also are consistent with the use of policy for countercyclical stability (primarily through spending and deficits), fiscal response to voter turnout, the growth of both spending and deficits under larger governing majorities and compliant monetary response to fiscal deficits.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.956
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.208
Teacher spread0.191 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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