Décompositions des mesures d'inégalité : le cas des coefficients de Gini et d'entropie
Bibliographic record
Abstract
Résumé Les mesures d’inégalité du revenu rassemblent deux types d’indicateurs décomposables : les indices décomposables en sous-populations et les indices décomposables en sources de revenu. Les premiers permettent de partager l’inégalité totale en une inégalité intragroupe et une inégalité intergroupe et les seconds d’attribuer à chaque facteur de revenu (revenu du travail, revenu du capital, taxes, etc.) une part de l’inégalité totale. Dans cet article, nous examinons d’une part la construction de ces techniques et d’autre part nous relatons les débats auxquels elles ont aboutis et plus particulièrement celui de la convergence vers un emploi simultané des deux types de décomposition. Classification JEL – D63, D31.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".