Endettement des provinces canadiennes : analyse comparative avec les entités fédérées des Etats-Unis, de l’Australie, de l’Allemagne et de la Suisse
Bibliographic record
Abstract
Cet Article analyse l’endettement Public des provinces canadiennes, qui sont aux États subnationaux des Etats-Unis (États), de l’Australie (États), de l’Allemagne (länder) et de la Suisse (cantons), en plus des pays de l’OCDE. L’étude procède au classement des États Fédérés les plus endettés selon différents concepts de dette publique. Les résultats révèlent que toutes les provinces canadiennes, à l’exception de l’Alberta, figurent parmi les entités fédérées les plus endettées. La répartition des dettes fédérales entre les entités fédérées ainsi que les concepts de dette étudiés affectent toutefois significativement le classement. This paper studies the public indebtedness of Canadian provinces in a comparative perspective with US states, Australian states, German länder and Swiss cantons, in addition to OECD countries. It presents Rankings of the most indebted subnational jurisdictions based on different public debt concepts. Results reveal that all provinces, Alberta excepted, are among the most indebted subnational states. However, how the federal debt is allocated among subnational states and the specific debt concept being considered significantly influence the rankings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".