Correlates of flood preparedness in urban households: Evidence from the Greater Accra Metropolitan Area of Ghana
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".