Seasonal patterns of severe food shortages vary by region in Ghana
Bibliographic record
Abstract
Agriculture‐dependent populations often experience seasonal food insecurity. The perception of severe food shortages was documented in 6 rural and 6 semi‐rural communities in 3 regions of Ghana. Data were collected through interview‐administered questionnaires with 845 households. There were significant regional differences in the reported pattern of severe household food shortages (p<0.05). Northern communities reported a pattern that was moderately high throughout the year, around 15% of households, and a peak in May. Mid‐country communities showed consistently high shortages, around 40%, with little monthly variation. The coastal communities reported little to no food shortages from August through February with a sharp peak in May to June. There were significant differences between rural and semi‐rural communities. Prevalence of food shortages was higher for semi‐rural communities in the North and Coast during the peak months (p<0.05), but higher in the rural North in November and December (p<0.05). Understanding regional and locale differences that influence access to food is essential to enhancing food security in Ghana. Support was through GL‐CRSP, funded in part by USAID, Grant # PCE‐G‐00‐98‐00036‐00, and a CIHR grant to Harding.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".