Gouverner en temps d'incertitude : Renforcer la résilience budgétaire et optimiser la dette publique
Bibliographic record
Abstract
Face aux incertitudes économiques croissantes, cet article analyse comment les gouvernements peuvent renforcer leur résilience budgétaire et optimiser la gestion de leur dette publique. À travers une approche comparative du Brésil, du Canada et de la Côte d’Ivoire (2019-2023), marquée par la crise de la COVID-19, il explore les stratégies adoptées pour faire face aux chocs économiques. S’appuyant sur des données de la Banque Mondiale, du FMI, de l’OCDE et de l’International Budget Partnership (IBP), l’analyse met en évidence le rôle clé de la flexibilité institutionnelle et de l’adaptabilité des politiques publiques. En s’inspirant des expériences de la Suède dans les années 1990 et de l’Australie face aux crises de 2008 et 2020, l’article identifie les leviers d’une gouvernance budgétaire efficace en période de turbulence. Il propose ainsi des pistes pour concilier stabilité financière et capacité d’adaptation face aux crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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; both teacher heads agree on what is shown here.
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".