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Record W2789113431 · doi:10.5539/res.v10n1p46

Candidate Selection Methods, Cooperation and Legislative Effectiveness

2018· article· en· W2789113431 on OpenAlexvenueno aff
Osnat Akirav

Bibliographic record

VenueReview of European Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureParliamentLegislationPoliticsSelection (genetic algorithm)CategorizationPolitical scienceCompromiseVariable (mathematics)Public administrationBusinessLaw and economicsLawEconomicsComputer science

Abstract

fetched live from OpenAlex

This study investigates whether differences in candidate selection methods and/or the changes in the Israeli political system affect cooperation between parliament members and whether such cooperation explains legislative effectiveness. To examine these issues, we discuss different types of cooperative strategies using a scale we devised that defines three options for cooperation: 1) uncooperative, 2) cooperation within the party and 3) cooperation between parties. Then, we categorize the various methods that Israeli political parties have used to select their candidates since the establishment of the state, creating four categories of a variable we call the effect of the primaries. Additionally, we consider differences in four periods of changes in the Israeli party system. Finally, we assess the results of cooperation in light of our dependent variable, legislative effectiveness, using data from 1949 to 2015. Our findings indicate that the majority of bills passed without cooperation, but when cooperative strategies were used, they usually involved inter-party rather than intra-party support. Furthermore, the adoption of primaries reduced the probability of passing bills. In addition, when one party was dominant, 68% of the representatives initiated legislation alone, while during the multi-polar fragmented period 41.9% cooperated with legislators from other parties.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.857
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.111
GPT teacher head0.497
Teacher spread0.386 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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