Test empirique d’explications de l’évolution des dépenses publiques au Canada 1960-1990
Bibliographic record
Abstract
Cet article a pour objectif de tester empiriquement plusieurs modèles d’explication des dépenses publiques. Nous confrontons certaines variables d’explication tirées des théories démocratiques aux variables tirées de l’approche Public Choice dans le but d’évaluer la contribution de ces dernières à l’explication de l’évolution des dépenses réelles du gouvernement fédéral. Nos résultats indiquent que, parmi les variables démocratiques retenues pour analyse, la variable chômage contribue largement à l’explication des variations annuelles dans les dépenses publiques. Parmi les variables Public Choice, l’illusion fiscale et les cycles électoraux contribuent de façon significative à expliquer les variations dans les dépenses publiques, même si cette contribution est faible.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".