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Record W2781736082 · doi:10.24124/c677/20171254

Public opinion in Quebec under the Harper Conservatives

2018· article· en· W2781736082 on OpenAlexaffvenueabout
Maxime Héroux-Legault

Bibliographic record

VenueCanadian Political Science Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMandateGovernment (linguistics)FederalismDemocracyPolitical sciencePublic administrationPower (physics)Public opinionFiscal imbalancePosition (finance)LawEconomicsPoliticsFiscal policy

Abstract

fetched live from OpenAlex

At the beginning of their mandate, the Harper Conservatives made several attempts to convince Quebecers to support their government. They gave Quebec a voice at the UNESCO and adopted a motion recognizing Quebec as a nation. They also promised to rectify the fiscal imbalance and to not use the federal spending power in provincial jurisdictions. Yet, these attempts failed to translate into increased electoral support for the Conservatives. 
 
 Given this puzzle, the paper analyzes trends in public opinion to identify what measures promoted by the Conservative government were supported by Quebecers, and on what issues they are in disagreement. The paper also analyzes the levels of satisfaction with the federal government and democracy among Quebecers during the time period. 
 
 The results show that on most issues, Quebecers have become more distant from the Conservative government’s position over the years. On issues such as scrapping the gun registry and the place of Quebec in the federation, Quebecers disagreed more strongly with their federal government in 2011 than in 2006. Likewise, their satisfaction towards both the federal government and democracy declined during this time period. These trends explain why the Conservatives have been unable to win more votes in Quebec despite their commitment to open federalism.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.121
GPT teacher head0.377
Teacher spread0.256 · 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 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

Citations0
Published2018
Admission routes3
Has abstractyes

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