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Record W2158360620 · doi:10.29173/agora19898

The Unite the Right Movement and the Brokerage of Social Conservative Voices Within the New Conservative Party of Canada

2013· article· en· W2158360620 on OpenAlexaffvenueabout
Laura Devaney

Bibliographic record

VenueThe Agora Political Science Undergraduate Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConservatismRealigning electionAlliancePolitical sciencePolitical economyPopulationBalance (ability)PoliticsBannerVotingLawLaw and economicsSociologySocialismCommunism

Abstract

fetched live from OpenAlex

2003 marked a year of significant change in the political landscape, particularly for the Canadian right. After ten years of division, the Progressive Conservative Party of Canada (PCs) and the Canadian Alliance united to create the Conservative Party of Canada. This union required a balancing of the interests of both of its founding parties who, on certain issues, espoused very different views. One important example of this was social conservatism. In this paper, the author examines the new party’s attempt to balance the two parties’ differing opinions on social conservatism. In order to accomplish this, the paper first examines the differences between the two parties, and then examines how conflicting interests were resolved under the banner of the ‘New Conservative Party.’ The author concludes that by deliberately declarin socially conservative issues beyond the scope of party policy, the new conservative party has been able to strike a balance between the interests of the voting population and the interests of its more socially conservative members, who are able to express their preferences by means of a free vote.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0280.013
Scholarly communication0.0080.001
Open science0.0010.004
Research integrity0.0020.003
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 head0.015
GPT teacher head0.280
Teacher spread0.264 · 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 designQualitative
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
Published2013
Admission routes3
Has abstractyes

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