ADAPTING THE UNDP MPI TO DEVELOP A NEW MULTIDIMENSIONAL MEASURE OF CIRCUMSTANTIAL POVERTY FOR HARARE PROVINCE, ZIMBABWE
Bibliographic record
Abstract
The objective of this study is to construct a new Multidimensional Poverty Index (MPI). To achieve the objective, the study uses the United Nations Development Programme (UNDP) MPI to produce a new multidimensional poverty measure using dimensions and indicators from the United Nations Sustainable Development Goals (SDGs) for 2030. The province of Harare in Zimbabwe is used a reference point in this study. Alkire-Foster Method (AF) methodology is used to model the logistic regression which employs 36 indicator variables across various dimensions of poverty. The analysis based on the logistic regression reveals the following insights. First, the MPI for Harare province is 37%. Moreover, Harare MPI by location is estimated to be: Harare rural - 50%, high density locations - 42%, medium density - 25% and low density is 15%. The analysis reinforces the generally accepted belief that the Harare province is witnessing an extremely high level of poverty. The extreme level of poverty in the province of Harare requires immediate interventions for the country to achieve SDG targets of reducing poverty in all its dimensions in a dedicated approach that ensures that no human lives under the poverty line (UN, 2019). The first port of call is to reduce poverty in rural locations in Harare report a head count poverty of 100% where the need for policy and programmatic interventions is most pronounced.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".