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Record W2900253290 · doi:10.29173/psur15

Parliamentary Reform in Canada: The Significance of Senate Reform

2016· article· en· W2900253290 on OpenAlexvenueaboutno aff
Navneet Gidda

Bibliographic record

VenuePolitical Science Undergraduate Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyInstitutionPublic administrationStatus quoState (computer science)Government (linguistics)Political sciencePower (physics)Corporate governanceReform ActPlan (archaeology)LawPoliticsEconomicsManagement

Abstract

fetched live from OpenAlex

In this paper, I will argue that the Canadian Parliamentary system has become significantly less democratic over time and therefore requires reform. Specifically, I will focus on the Senate and the ways in which the institution has had a negative impact on the state of Canadian democracy. Through an analysis of how Senators are selected, the make up of the Senate, and the institution’s role in Canadian governance, I come to the conclusion that Canadians must demand reform if they are to maintain a strong, healthy democracy that serves their interests. Mainly, I support a Triple E Senatorial system since it gets at the root of the problem by decentralizing federal power and giving it to the provinces and Canadian people. I also include a brief discussion of Justin Trudeau’s plan for the Senate which proposes more immediate reform and does not require constitutional revision. Rather than demanding abolition or tolerating the status quo, taking these steps towards reform will ensure that Canadian interests are the government’s top priority. Through reform, Canadians would have more effective “sober second thought” and a democracy that works for the people, not the party in power.

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)0.011
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.895
Threshold uncertainty score0.761

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0100.010
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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 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

Citations0
Published2016
Admission routes2
Has abstractyes

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