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Record W2601563381 · doi:10.57912/23836155

Why Bills (Don't) Become Law: The Success and Failure of Government Legislation in Parliamentary Democracies

2023· article· en· W2601563381 on OpenAlexaboutno aff
Andrew McKelvy

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationGovernment (linguistics)Political scienceLawPublic administration

Abstract

fetched live from OpenAlex

Scholarship on executives’ successes in enacting bills that they propose to their legislatures has emphasized the direct effects of institutional arrangements and the political effects of electoral and coalition-formation results. It has also largely focused on presidential democracies (to the exclusion of their parliamentary counterparts) and on aggregate-level rates of success (to the exclusion of individual bills). In addressing these shortcomings, I focus on executives as strategic actors who must and, even when faced with adverse conditions, are able to 1) ensure that their bills receive sufficient support, 2) receive formal expression of that support, and 3) desire enactment as an important goal. The latter two points have been largely ignored by work on legislative success, and I advance the theoretical understanding of the first by applying insights from Tsebelis’ (2002) veto players theory. In a sample of 14 parliamentary democracies, I find that executives’ rates of success are in fact not adversely affected by a number of factors that might seem to pose obstacles. In addition, I examine over 500 individual government bills introduced in the Canadian parliament, finding that, while initial lack of support reduces the likelihood of a bill’s passage, it also makes the government more willing to accept amendments on bills. The acceptance of amendments, in turn, as well as efforts to expedite bills, increases the likelihood of enactment. I also find that the impact of government amendments is greater for bills that initially lack sufficient support than for bills that never lack sufficient support.

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)0.007
metaresearch head score (Gemma)0.036
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 score0.210
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0070.008
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.326
Teacher spread0.270 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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