Note sur quelques méthodes d’évaluation de l’inégalité dans la répartition des revenus par groupe, basées sur l’indice Gini
Bibliographic record
Abstract
In this paper, we compare three methods presently used to split up the Gini index in order to evaluate the contribution of one particular factor (for example, age) to the value of this index: the B-M-P decomposition*, Paglin's measures and Love & Wolfson indexes. The problem with the decomposition of the Gini index is that it is impossible to cut it in two parts, one, representing the value of inequality attributable to the factor analysed and the second, inequality due to other factors. We also have to include the value of overlaps. This is clearly shown by Bhattacharya and Mahalanobis. By using a very simple example for which we can forecast the results, we can compare the reaction registered by each method when we introduce a change in the distribution of income and consequently evaluate the lightness of these methods. We confirmed our convictions by decomposing two other measures which can be separated in the two parts mentioned above: Theil's entropy and the square of the coefficient of variation. We conclude that the indexes used in the B-M-P decomposition are exact. Paglin's age-Gini index is accurate, but not his residue, the Paglin-Gini's index. And, Love and Wolfson's index did not behaved as expected to our modifications. We also showed, by using the B-M-P decomposition, that overlaps is an important component. Finally, we noted that our indexes changed in value when we changed the number of groups analysed (example: if, to analyse the effect of age we divide our population into 5 or 10 age groups). So, it is important in a longitudinal study always to use the same group definitions to obtain comparable results. * Bhattacharya, Mahalanobis and Pyratt.
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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 | 0.044 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".