Risk factors of becoming a disaster victim. The flood of September 1st, 2009, in Ouagadougou (Burkina Faso)
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".