Multidimensional Poverty Index 2014: Brief Methodological Note and Results
Bibliographic record
Abstract
<p>The Multidimensional Poverty Index (MPI) 2013 uses the same parameters (dimensions, indicators, cutoffs and weights) and the same functional form as in previous years. The main innovation this year consists in updating the estimations for a series of countries and providing further possibilities for analysis over time. This brief methodological note outlines specific changes and clarifications concerning the MPI 2013 estimations, and presents the tables with the full results. It first explains the main updates in the MPI 2013 as well as the policies that will govern the MPI updates from 2013. It summarizes the MPI methodology that has been presented in detail in other documents (Alkire and Santos 2010; Alkire, Roche, Santos and Seth 2011). Then it explains the DHS nutritional subsamples and treatment for analysis over time. Finally, brief guidelines on how to undertake accurate analysis of changes over time are presented. The methodologies used to generate the tables on the MPI and the 104 country briefings and interactive maps available on OPHI’s website, as well as the results published in the 2013 Human Development Report, are presented in this note.</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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".