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Record W7073777636

Reaching across the aisle: Explaining government-opposition voting in parliament

2017· article· en· W7073777636 on OpenAlexaff

Bibliographic record

VenueLeiden Repository (Leiden University) · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsTrinity College
Fundersnot available
KeywordsParliamentCabinet (room)VotingIdeologyOpposition (politics)Variation (astronomy)
DOInot available

Abstract

fetched live from OpenAlex

The divide between government and opposition is clearly visible in the way members of parliament vote, but the variation in government–opposition voting has been left relatively unexplored. This is particularly the case for contextual variation in the extent to which parliamentary voting behaviour follows the government–opposition divide. This article attempts to explain levels of government–opposition voting by looking at three factors: first, the majority status of cabinets (differentiating between majority and minority cabinets), cabinet ideology (differentiating between more centrist and more extremist cabinets) and norms about cabinet formation (differentiating between wholesale and partial alternation in government). The study includes variation at the level of the country, the government and the vote. The article examines voting in the Netherlands (with a history of partial alternation) and Sweden (with a history of wholesale alternation). We find strong support for the effect of cabinet majority status, cabinet ideology and norms about cabinet formation on government–opposition voting.

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)0.012
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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