Multidimensional Poverty Measurement and Analysis: Chapter 5 - The Alkire-Foster Counting Methodology
Bibliographic record
Abstract
This chapter provides a systematic overview of the Alkire-Foster multidimensional measurement methodology with an emphasis on the Adjusted Headcount Ratio denoted <strong>(<em>M</em><sub>0</sub>)</strong>. The chapter is divided into seven sections. The first shows how this measure combines the practical appeal of the counting tradition with the rigor of the axiomatic one. The second sets out the identification of who is poor using the dual-cutoff approach, and the third outlines the aggregation method used to construct the Adjusted Headcount Ratio. In the fourth, we take stock and present the main distinctive characteristics of the Adjusted Headcount Ratio, whereas the fifth section presents its useful, consistent partial indices or components. To illustrate, we present a case study using the global Multidimensional Poverty Index (MPI) in the sixth section. The final section presents the members of the AF class of measures that can be constructed in less common situations where data are cardinal.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".