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Record W7056051615

Electoral Reform in Canada

2024· article· en· W7056051615 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentProportional representationElectoral reformPrime ministerElectoral systemRepresentation (politics)Order (exchange)Electoral geographyLower house
DOInot available

Abstract

fetched live from OpenAlex

Canada's relationship with electoral reform is a compelling study, as the inherited British electoral system, single member plurality (SMP), is not well suited for Canada's electoral climate. In order to properly represent Canada's political climate and diversity, mixed member proportional representation (MMP) would pose a better option for an electoral system, allowing Canada to keep regional representation while still incorporating proportional representation. Looking at New Zealand's adaptation of mixed member proportional over 25 years is a good start for researching how this could be adapted in Canada, as well as researching the benefits of multi-party systems and occasional coalition governments since these are often considered the side effects of proportional representation. Finally, researching Britain's success of having more members of Parliament to reduce the power of the Prime Minister and the Party Whip should be crucial when deciding how Canada should incorporate MMPs' list MPs feature. This research leads me to the conclusion that it would be beneficial for Canada to change its electoral system from SMP to MMP, by creating more seats in parliament to accommodate list MPs instead of merging electoral districts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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 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
Published2024
Admission routes1
Has abstractyes

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