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Record W2266989898

Political Parties in Canada: What Determines Their Entry, Exit and the Duration of Their Lives?

2016· preprint· en· W2266989898 on OpenAlexaboutno aff
J. Stephen Ferris, Marcel Voia

Bibliographic record

VenueCarleton University's Institutional Repository (MacOdrum Library, Carleton University) · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsMargin (machine learning)PoliticsCompetitor analysisImmigrationChurningHazardEconomicsPolitical economyHazard modelDemographic economicsPolitical sciencePublic economicsLabour economicsLaw
DOInot available

Abstract

fetched live from OpenAlex

In this paper we consider two margins of individual political party life in Canada since Confederation—
\nthe extensive margin governing existence (the entry and exit decisions, together with party turnover or
\nchurning) and the intensive margin determining lifespan or survival length. The results on the extensive
\nmargin confirm in a more formal way many of the individual hypotheses advanced in the political
\nliterature for entry and exit—the importance of voter heterogeneity, minority governments, world wars,
\nnumber of competitors and economic circumstances. What stands out most strongly in the data is the
\nintroduction of public funding for established political parties following 1974 and recent immigration
\nflows. The intensive margin is explored using a number of hazard models before narrowing choice to
\nsemi-parametric models. Potential endogeneity is dealt with by using a discrete hazard model with
\ndiscrete finite mixtures. This form best captures the empirical hazard, allowing for the detection of party
\ntype heterogeneity while being agnostic with respect to the correlation between observables and this
\nspecific type of heterogeneity. The results suggest the presence of two distinct political party types and,
\nmore generally, mirror the results found on the extensive margin.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
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 head0.017
GPT teacher head0.211
Teacher spread0.193 · 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 designTheoretical or conceptual
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

Explore more

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