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Record W3115877478 · doi:10.1080/02722011.2020.1811585

What Should Be Done with a “House in Order”? An Economic Perspective on Post-Liberal Quebec

2020· article· en· W3115877478 on OpenAlexaffabout
Peter Graefe, X. Hubert Rioux

Bibliographic record

VenueThe American Review of Canadian Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsÉcole Nationale d'Administration PubliqueMcMaster University
Fundersnot available
KeywordsPerspective (graphical)Order (exchange)Public administrationPolitical scienceSociologyEconomicsArtFinance

Abstract

fetched live from OpenAlex

The Quebec provincial election of October 2018 marked the end of an era. It saw the Quebec Liberal Party (QLP) suffer the worst defeat of its history and the election of agovernment, the first in a half century, not headed by either the Liberals or the Parti Québécois. The election of the Coalition Avenir Québec (CAQ) suggests a political realignment. Yet, whether or not the 2018 vote also ended the era of budget austerity remains uncertain. Combined with a modest growth in program spending, public-sector reforms and cutbacks might, under the new administration, affect public services and once again generate opposition and resistance to budget austerity. In this article, we address this question of change versus continuity in three steps. First, we provide a retrospective account of budget policies under recent Liberal governments in Québec. Second, we present and discuss the CAQ’s evolving views on public budgeting and economic policy. Finally, we analyze the new government’s actions in its first year in office. While it is hard to discern a clean break in budgetary policies, the CAQ government might surprise us yet: it has a leader largely unconstrained by his party and whose ideas about budgeting and the public sector depart from neoliberal mantras. In addition, he has nationalist policy ideas, particularly around economic development, that might spur significant departures if they are aggressively implemented

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.077
GPT teacher head0.376
Teacher spread0.299 · 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.

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

Citations6
Published2020
Admission routes2
Has abstractyes

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