Correlates of flood preparedness in urban households: Evidence from the Greater Accra Metropolitan Area of Ghana
Why this work is in the frame
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Bibliographic record
Abstract
The annual floods in cities in Sub-Saharan Africa are exacerbated by the impacts of climate change. For coastal cities double flood burden from storms and sea level rise are phenomenal and in response, data is gradually emerging on the exposure of urban areas and households’ adaptation of which population determinants are mostly omitted. This paper uses a household survey of flood experiences, analyzed with the Tobit model to understand the social and demographic factors that drive households' preparedness for floods in the Greater Accra Metropolitan Area in Ghana. Findings show that the age and income of the household head and planned adaptation significantly increased the likelihood of households’ preparedness for floods. While community access to financial assistance reduced the likelihood of household preparedness, membership in social support groups and the availability of community-level social amenities and shelters increased the likelihood of household preparedness by 0.81 units (p<0.05), 1.72 units (p<0.01) and 1.33 units (p<0.01) respectively. Therefore, enhanced education and awareness of flood risks are major factors of flood disaster risk reduction amidst neighborhood networks towards scaling the relevance of anticipatory flood contingency planning in coastal urban planning and management and a recipe for mainstreaming the Sendai Framework for Disaster Risk Reduction.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it