Multidimensional poverty and the post-2015 MDGs: OPHI research briefing 11
Bibliographic record
Abstract
<p>This brief proposes the consideration of a Multidimensional Poverty Index (MPI) 2.0 (now known as the MPI 2015+) in post-2015 MDGs, as a headline indicator of multidimensional poverty that can reflect participatory inputs, and can be easily disaggregated.</p>\n\n<p>Most projections suggest ending $1.25/day poverty would not require much in the way of bending the current trend – so it is achievable. But ending $1.25/day poverty is unlikely to mean the end of the many overlapping disadvantages faced by people living in poverty, including malnutrition, poor sanitation, a lack of electricity, or ramshackle schools.</p>\n\n<p>This brief considers what the MPI, reflecting acute multidimensional poverty, could offer in the context of the post-2015 MDG discussions. Granted there will be other goals – for example, to improve health – each having a bevy of indicators. Yet alongside these, a headline MPI could provide an eye-catching and intuitive overview measure, with easily understood and consistent details on its component indicators. Indeed, an MPI 2015+ could be formed from a ‘voices of the poor’ type participatory exercise.</p>\n\n<p>The MPI 2015+ would complement a $1.25/day measure by showing how people are poor (what disadvantages they experience); in which regions or ethnic groups they are poor; and the inequalities between those living in poverty. It would add value for policymakers, providing political incentives to reduce poverty by reflecting changes swiftly; it could also be used to monitor inclusive growth, and to show the nexus between challenges of poverty and sustainability.</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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".