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Record W3122164065

Fixed versus Flexible Election Cycles: Explaining innovation in the timing of Canada’s Election Cycle

2016· preprint· en· W3122164065 on OpenAlexaboutno aff
J. Stephen Ferris, Derek Olmstead

Bibliographic record

VenueCarleton University's Institutional Repository (MacOdrum Library, Carleton University) · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)LegislatureSurpriseExternalityDisadvantageLegislationEconomicsPoliticsPolitical economyPublic administrationPublic economicsPolitical scienceMicroeconomicsLawSociology
DOInot available

Abstract

fetched live from OpenAlex

This paper argues that there is an efficiency gain underlying the recent adoption of legislation calling for a fixed four-year governing term by the federal and most provincial governments in Canada. The efficiency gain arises from foreclosing an externality produced by the constitutional provision that sets a maximum length for a legislative term (five years) while allowing the governing party (through the Governor General) to dissolve the House early. Because the opportunistic use of surprise can improve the governing party’s probability of winning, strategic choice can lead to elections being held at times that most disadvantage the incumbent’s rivals. Evidence from Canada is introduced suggesting that federal elections became less predictable through successive reductions in the campaign time given to competitors, thus raising the cost of this externality. The same reasoning suggests that the party most likely to propose this legislative innovation will be the party in opposition rather than in power and/or the new leader of an established party facing loss in the upcoming election. By fulfilling the fixed term even when it could benefit by calling the election early, the party establishes a precedent that raises the political cost to others of cancelling the fixed term legislation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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