Multidimensional poverty and the post-2015 MDGs: OPHI research briefing 11
Bibliographic record
Abstract
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. \n\n 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. \n\n 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. \n\n 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.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".