Public Perception and Resilience to Flooding: An Examination of Vulnerability in Brazzaville, Congo During the 2019 Floods
Bibliographic record
Abstract
This study assesses the risk perception of flooding victims, sensitivity and exposure of people to flooding between November and December 2019 in Brazzaville, Congo.To achieve our objective, a questionnaire was administered to 382 people living in the zone of our research.Methodologically, an analysis of socio-economic indicators such as age, level of education, income, gender, risk exposure indicators (location, house typology, height of water, duration of flooding, etc.), sensitivity indicators (time in the area, cause of flooding, experience of flooding, type of house, etc.) as well as adaptation measures (risk response, adaption strategies, return to normal, relief and help, etc.) were done and used to highlight the extent of recovery of flood victims.Based on the findings of this study, it came out that 71.5% of those surveyed were not aware of the risks, while 28.5% had some few knowledge of the risk.The study also found that 72.2% of people living in flood-prone areas were not aware of the risks of flooding in their location.During that period, the Government of Congo provided supplies and palliatives such as food, medication, and temporary housing to some victims.Sequel to the findings of this study, it is recommended that the government of Congo (Brazzaville) works on heightening awareness regarding flood risks and provide indicators that can be used to enhance cities' resilience against flooding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".