Conditions for the Most Robust Poverty Comparisons Using the Alkire-Foster Family of Measures
Bibliographic record
Abstract
<p>In the burgeoning literature on multidimensional poverty indices, the Alkire-Foster (AF) measures stand out for their resilience in identifying the multidimensionally poor with cut-off criteria covering the spectrum from the union approach to the intersection approach. The intuitiveness and easy applicability of the measures’ identification and aggregation methods are reflected in the increasing use of the AF measures in poverty measurement, as well as in other fields. This paper extends the dominance results derived by Lasso de la Vega (2009) and Alkire and Foster (2010) for the adjusted headcount ratio and develops a new condition whose fulfillment ensures the robustness of comparisons using the adjusted headcount ratio for any choice of multidimensional cut-off and for any weights and poverty lines. The paper then derives a first-order dominance condition for the whole Alkire-Foster family (that is, for continuous variables).</p>
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".