Demand‐Side Challenges to Poverty Monitoring and Assessment Systems: Illustrations from Tanzania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past decade, considerable attention and resources have been directed at Poverty Monitoring and Assessment Systems (PMASs), a core problem being the limited demand for, and use of, the data they generate. This article discusses the sources of these demand‐side problems and explores the difficulties in trying to address them via PMAS‐related processes, arguing that both institutional factors and design features have contributed to the disappointing performance of these systems. Incentive structures within the public sector in particular have made for an extremely unfavourable environment, and institutional problems have been compounded by questionable design features resulting from faulty analysis, flawed assumptions and conflicting views as to the objectives of ‘poverty monitoring’. Tanzania's PMAS experience is used to illustrate the argument.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it