Measuring Chronic Multidimensional Poverty: A Counting Approach
Bibliographic record
Abstract
How can indices of multidimensional poverty be adapted to produce measures that quantify both the \njoint incidence of multiple deprivations and their chronicity? This paper adopts a new approach to the \nmeasurement of chronic multidimensional poverty. It relies on the counting approach of Alkire and \nFoster (2011) for the measurement of multidimensional poverty in each time period and then on the \nduration approach of Foster (2009) for the measurement of multidimensional poverty persistence across \ntime. The proposed indices are sensitive both to (i) the share of dimensions in which people are deprived \nand (ii) the duration of their multidimensional poverty experience. A related set of indices is also \nproposed to measure transient poverty. The behaviour of the proposed two families is analysed using a \nrelevant set of axioms. An empirical illustration is provided with a Chilean panel dataset spanning the \nperiod from 1996 to 2006.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".