Food poverty assessment in Ghana: A closer look at the spatial and temporal dimensions of poverty
Bibliographic record
Abstract
The multifaceted nature of poverty in terms of its duration or chronicity, systematic changes, seasonality, variation, and risk or vulnerability makes its measurement and analysis complicated, especially in lower-income countries. In Ghana, data show that absolute poverty remains prevalent, and inequality has been rising. Despite the gradual decline in poverty, spatial income inequality has also become a concern in Ghana. This study develops a Foster-Greer-Thorbecke Poverty Measure based spatiotemporal model to investigate the variation in food poverty in Ghana. Application to population-based surveys fielded in 2012/13 and 2016/17 indicate that considerations of temporal and spatial dimensions of poverty have implications for gaging the level of deprivation among households and the potential allocation of scarce resources via policy to achieve poverty alleviation objectives. A model that jointly considers both the spatial and intra-annual dynamics arguably considered the most accurate and flexible but data-intensive one, resulted in the mean unconditional food poverty rate of 50%, with the lowest rate being the Northern Region in March (45%) and the highest rate being in the Upper West Region in June (54%). Overall, cost-wise, this flexible model also results in the highest potential cost savings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".