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Record W2994030065

Measuring the Effectiveness of a Minority Parliament

2007· article· en· W2994030065 on OpenAlexaboutno aff
Paul Thomas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentLegislatureProportionality (law)DeliberationDemocracyPublic administrationPolitical scienceGovernment (linguistics)PoliticsPolitical economyContext (archaeology)Lower housePublic economicsLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

Canada¿s plurality electoral system often allows parties earning less than half of the popular vote to receive a majority of Parliamentary seats. Several analysts have suggested that this problem should be corrected by changing the electoral system to increase the proportionality between a party¿s share of the vote and its share of legislative seats. However, while this type of reform would increase proportionality, it would also greatly increase the frequency of minority governments. This paper uses the minority government that took place in the 38th Parliament as a test case to see how Canada¿s political system might be affected if the country adopts a new electoral system that produces minority governments more frequently. The paper sets out the procedural context of the 38th Parliament and develops six criteria for evaluating its behaviour. It then explores each criteria using a qualitative and quantitative comparison of the actions of the 36th, 37th, and 38th Parliaments. This evaluation shows that the 38th Parliament was no less efficient than its predecessors, featured greater legislative deliberation, and was better able to hold the executive accountable for its actions. As a result the paper concludes that while minority governments are by no means perfect, the example of 38th Parliament suggests that an electoral system which produced more minority governments could increase the quality of democracy in Canada.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.952
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.034
GPT teacher head0.301
Teacher spread0.267 · 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 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

Citations17
Published2007
Admission routes1
Has abstractyes

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