What Should Be Done with a “House in Order”? An Economic Perspective on Post-Liberal Quebec
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".