Risk factors of becoming a disaster victim. The flood of September 1st, 2009, in Ouagadougou (Burkina Faso)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In light of the expected growing natural hazards and the continued growth of urban populations, there is concern that the vulnerability of a significant portion of the urban African population will increase. The objective of the paper is to analyze factors associated with the status of “disaster victim” in Ouagadougou, the capital-city of Burkina Faso. On September 1st, 2009, this city experienced torrential rainfall leading to water runoffs and floods. Over 180,000 people were severely affected, about 41 people died and 33,172 houses completely destroyed. The data availability from the Ouagadougou Health and Demographic Surveillance System, especially characteristics of population dwellings before the flood, grant the opportunity to address the impact of this event among the different social groups. Modeling data with logistic regressions, the results reinforce the idea that the main cause of disaster is not hazards. Indeed, natural disaster amplify urban inequities given the role playing by variables related to extreme poverty (no sanitation, no electricity) as determinant factors. Discussion highlights how some households inhabitants make the reasoned choice of gradually reoccupying their plots, although aware of risks. In Sub-Saharan Africa, early warning system for floods should be seen as essential in urban settings.
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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 | 0.000 | 0.000 |
| 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 it