Conditions for the Most Robust Poverty Comparisons Using the Alkire-Foster Family of Measures
Bibliographic record
Abstract
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 fulfilment 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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".