Community perceptions on institutional gaps in flood disaster preparedness and management in Mepe community, Ghana
Bibliographic record
Abstract
Amidst the growing evidence of human-induced flooding disasters, disaster management institutions have become critical actors in mitigating flood vulnerability. This study investigates how the failure of disaster management institutions amplifies the impacts of flood disasters using the Social Amplification of Risk Framework and Hyogo Framework for Disaster Action. We addressed this problem based on insights from local leaders, flood victims, and other stakeholders about the 2023 Akosombo Dam spillage-induced flood disaster at Mepe Township in Ghana. The findings indicate that the flood disaster was an avoidable error caused by top-down institutional decision-making processes, amplified by the sociopolitical structures of disaster management institutions. Secondly, ineffective communication and coordination hindered disaster management efforts and increased the impact of flooding. Our findings underscore the need to prioritise community perspectives and ensure effective and locally tailored communication of disaster management strategies. We also recommended policies that enhance the capacity of disaster management institutions to prevent and effectively manage future flood disasters.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".