Bibliographic record
Abstract
Since the seminal work of Sen, poverty has been recognized as a multidimensional phenomenon. The recent availability of relevant databases renewed interest in this approach. This paper estimates multidimensional poverty in fourteen Sub-Saharan African countries using the Alkire-Foster multidimensional poverty measures, whose identification method is based on a counting approach. Four dimensions are considered: assets, health, schooling and empowerment. The results show important differences in poverty among the countries of the sample. The multidimensional poverty estimates are compared with some standard measures such as the Human Development Index (HDI) and the income poverty headcount ratio. It is found that including additional dimensions into the analysis leads to country rankings different from those obtained with the two standard measures. Geographical decompositions and dimensions-break down indicate that rural areas are significantly poorer than urban ones and that schooling is in general the highest contributor to poverty. Finally, robustness and sensitivity analyses are done with respect to the number of dimensions in which deprivation is required so as to be considered poor (the across-dimensions cutoff) as well as to the poverty lines within each dimension. Several cases of dominance between countries are found in the first robustness test.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".