Multidimensional Poverty Index 2014: Brief Methodological Note and Results
Bibliographic record
Abstract
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.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".