A Unified Framework to Measuring Inequality in The Arab Countries
Bibliographic record
Abstract
The purpose of this paper is to apply a general and unified approach to inequality measurement in Arab countries. To this end, a wide class of inequality indices, proposed by Olmedo et al. (2009) and based on the Bonferroni (1930) curve, rather than the Lorenz curve, is used. When local measures of inequality are aggregated using an appropriate weighting system, familiar indices such as the Gini index can be retrieved. The choice of the weighting system yields a variety of inequality measures that depend on which part of the income distribution the overall inequality index is focused. Our framework offers a reassessment of inequality trends in the Arab world. Our results show that whatever the trend of inequality experienced by the selected Arab countries, the poorest people do not seem to be much affected by the changes in the inequality patterns. For instance, when some countries undergo a rise in overall inequality, changes in the inequality experienced by the poorest population are less pronounced. Inversely, when inequality decreases, the richest percentiles seem to become locally more equal than poorer ones. These findings imply that change in the average income of the poorest is generally very low.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".