Multidimensional measurement of poverty in Sub-Saharan Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Since the seminal works of Sen, poverty is recognized as multidimensional phenomenon. Recently, there is a renewed interest in this approach since relevant databases became available. Several methods of aggregation have been suggested to measure poverty in this way. Up to now, there is no consensus on the best measure. However, a suitable measure should satisfy some useful properties. Alkire and Foster (2007) propose a multidimensional poverty measure using a counting approach. This method is applied to estimate multidimensional poverty in fourteen Sub-Saharan African countries. Poverty identification is based on four dimensions (assets, health, schooling and empowerment). The main results show important differences in poverty among the countries of the sample. The findings are compared with some standard measures such as Human Development indicators (HDI) and the income poverty among others. Comparisons show that consider additional dimensions leads to country rankings different from the standard-based rankings. Poverty is also decomposed by rural and urban location and by dimension. Rural areas are identified obviously as the poorest while schooling appear to be in general the most contributor in poverty. Finally, some robustness and sensitivity analyses are done.</p>
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 it