Origen de la desigualdad y la progresividad en la financiación de las provincias y territorios canadienses
Bibliographic record
Abstract
El presente trabajo tiene por objeto analizar la contribución de las distintas \nfuentes de ingresos de los gobiernos provinciales y territoriales del Canadá a \nla desigualdad y la progresividad del sistema de financiación en su conjunto. \nAplicando la descomposición aditiva de los índices de Gini y de Suits los \nresultados muestran que, de partida, los recursos tributarios ya muestran una \nelevada progresividad en su reparto. Las transferencias aumentan ligeramente \ntanto la desigualdad como la progresividad propia de los tributos. Asimismo, \nson las transferencias de nivelación y el impuesto personal sobre la renta, con \neste orden, los que, con diferencia, contribuyen más tanto a la desigualdad \ncomo a la progresividad del conjunto del sistema de financiación._________________________________The present paper has as aim to analyse the contribution to the inequality \nand progressiveness of financing resources of Canada´s Provinces and Territories. \nAplying the additive decomposition of the Gini and Suits indexes, the results \nshow that the tax resources have high progressiveness in its distribution. The \ntransfers increase lightly the inequality and the progressiveness of the tax \nresources. Likewise the equalisation transfer and the personal income tax the \nresources, in this order, contribute to the inequality and progressiveness of the \noverall financing system.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".