To live with floods or not: Intersectionality of drivers of urban households' adaptation and relocation intentions
Bibliographic record
Abstract
Abstract The intent of households to relocate amidst floods in Ghana's Greater Accra Metropolitan Area, using combined socio‐demographic and physical factors is analyzed within 1206 households. The National Master Sampling Frame of Ghana's Population and Housing Census is utilized for the sampling. The Probit estimation technique is employed to understand the intersectionality of social, economic, demographic, and physical considerations influencing households' decision‐making regarding relocation amidst flood risks. The findings show households' reluctance to relocate contrary to relocation considered mostly as preferred adaptation. The likelihood of relocating exhibited a non‐linear pattern, decreasing only when a population was younger until age 55 before reversing. Indigenous households preferred not to relocate. In communities where place attachment and revenue sources significantly impacted relocation decisions, households with secondary education, past flood experiences, and non‐indigenous status influenced higher perception of flood risk. Therefore, relocation as an effective global adaptation strategy to floods is not widespread. Thus, empowering households to accept a certain level of flood risk potentially avoids maladaptation and involves a combination of hard infrastructure measures and regulatory approaches in places of residence that do not compromise livelihoods. However, if relocation becomes necessary, a right‐based approach must be favored over an absolute risk‐based approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".