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Record W7159894879

2015年カナダ連邦選挙の分析

2016· article· ja· W7159894879 on OpenAlexaboutno aff
Nobuaki Suyama

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2016
Typearticle
Languageja
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsBandwagon effectVictoryBattleLiberal PartyLiberalismBalance (ability)General electionDemocracy
DOInot available

Abstract

fetched live from OpenAlex

In the most recent election for the House of Commons, the Liberal Party led by Justin Trudeau scored a resounding victory over the Conservative Party led by Stephen Harper. Harper had held leadership over Canada for nearly a decade. When the campaign started, the Conservatives, the New Democratic Party (NDP), and the Liberals seemed to be in a tight race. It was not an unrealistic scenario that Canada would have the first-ever NDP government. However, as time went by, the popular support for the NDP went on the wane with the Tories remaining in the last-stage battle with the Grits. In the home stretch, Trudeau successfully outdistanced Harper. The Liberals now occupy 184 seats in the lower house, which clearly goes over the majority line. The Greens kept the leader's seat on Vancouver Island. The balance theory and the bandwagon theory offer little to explain the Liberals' win in Ottawa. It was indeed the young, good-looking Trudeau that appealed to the Canadian voters to change the governing party but there are more to explain the outcome of the election. The Liberals were able to expand their range of support rightward and leftward to snatch the votes from the Conservatives and the NDP. The swaying pledges were mainly domestic policy, mixed subtly with foreign policy. Trudeau's Liberal Party was able to persuade nearly 40 percent of the sensible Canadian voters of an alternative way Canada should move forward from the predecessor.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3850.258

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 head0.025
GPT teacher head0.292
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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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