Capturing nested spheres of poverty: a model for multidimensional poverty analysis and monitoring
Bibliographic record
Abstract
In this paper the authors discuss recent trends in poverty concepts and suggest a locally adapted multidimensional model for measuring and monitoring poverty. The model comprises nested layers with subjective wellbeing in the centre surrounded by a core of health, wealth and knowledge, and a context that includes natural, economic, social and political spheres, as well as service and structural aspects. These nine facets of poverty cover basic needs, individual assets and capabilities, and the enabling environment that helps people escape poverty by ensuring sustainability, providing opportunities and minimising vulnerability. The model was tested in several monitoring trials and in the official poverty and wellbeing monitoring of Kutai Barat District, Indonesia, in early 2006. Twenty-one subdistricts covering 223 villages with more than 150 000 people were assessed. Examples drawn from this experience illustrate possible applications of the model.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".