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Record W2416807665 · doi:10.1177/1078087415617302

Economic Voting and Multilevel Governance

2015· article· en· W2416807665 on OpenAlexaffabout
Cameron D. Anderson, R. Michael McGregor, Aaron Alexander Moore, Laura B. Stephenson

Bibliographic record

VenueUrban Affairs Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of WinnipegBishop's UniversityWestern University
Fundersnot available
KeywordsVotingGovernment (linguistics)DemocracyWork (physics)EconomicsCorporate governancePublic administrationPolitical sciencePublic economicsPolitical economyBusinessPoliticsFinance

Abstract

fetched live from OpenAlex

Past work has shown that economic conditions influence electoral outcomes at multiple levels of government in Canada and in democratic states around the world. However, there is significant variation in the jurisdictional ability of different governments to influence economic conditions; in particular, municipal governments may be least able to influence the economy. As a result, voters may be less likely to hold municipal incumbents accountable for economic conditions than either provincial or federal politicians. Building on this discussion, this article explores several questions. First, do citizens differentiate between the impacts of different orders of government on economic conditions? Second, does the economy affect incumbent support in local elections? Finally, does knowledge of the jurisdictional responsibilities of the three levels of government condition economic effects at the municipal level in Canada? We consider these questions using individual-level data collected during the 2014 Toronto municipal election.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.078
GPT teacher head0.354
Teacher spread0.275 · 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 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

Citations11
Published2015
Admission routes2
Has abstractyes

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