Does Previous Experience of Floods Stimulate the Adoption of Coping Strategies? Evidence from Cross Sectional Surveys in Nigeria and Tanzania
Why this work is in the frame
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Bibliographic record
Abstract
In sub-Saharan Africa, hydro-meteorological related disasters, such as floods, account for the majority of the total number of natural disasters. Over the past century, floods have affected 38 million people, claimed several lives and caused substantial economic losses in the region. The goal of this paper is to examine how personality disposition, social network, and socio-demographic factors mitigate the complex relationship between stressful life experiences of floods and ocean surges and the adoption of coping strategies among coastal communities in Nigeria and Tanzania. Generalized linear models (GLM) were fitted to cross-sectional survey data on 1003 and 1253 individuals in three contiguous coastal areas in Nigeria and Tanzania, respectively. Marked differences in the type of coping strategies were observed across the two countries. In Tanzania, the zero-order relationships between adoption of coping strategies and age, employment and income disappeared at the multivariate level. Only experience of floods in the past year and social network resources were significant predictors of participants’ adoption of coping strategies, unlike in Nigeria, where a plethora of factors such as experience of ocean surges in the past one year, personality disposition, age, education, experience of flood in the past one year, ethnicity, income, housing quality and employment status were still statistically significant at the multivariate level. Our findings suggest that influence of previous experience on adoption of coping strategies is spatially ubiquitous. Consequently, context-specific policies aimed at encouraging the adoption of flood-related coping strategies in vulnerable locations should be designed based on local needs and orientation.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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