Community vulnerability assessment index for flood prone savannah agro-ecological zone: A case study of Wa West District, Ghana
Bibliographic record
Abstract
The savannah regions of Northern Ghana are characterized by smallholder farming systems and high levels of poverty. Over the past two decades, communities in the regions have become more prone to climate and human-induced disasters in the form of annual floods and droughts. This study evaluates the degree and magnitude of vulnerability in four communities subjected to similar climate change induced flood events and propose intervention options. The study employs rural participatory research approaches in developing four vulnerability categories namely socio-economic, ecological, engineering and political; which were used to develop indicators that aided the calculation of total community vulnerability index for each community. The findings indicate that the state of a community's vulnerability to flood is a composite effect of the four vulnerability index categories which may act independently or concurrently to produce the net effect. Based on a synthesis of total vulnerability obtained in each community, Baleufili was found to be the least vulnerable to flood due to its high scores in engineering, socio-economic and political vulnerability indicators. Baleufili and Bankpama were the most ecologically vulnerable communities. The selection of vulnerability index categories and associated indicators were grounded in specific local peculiarities that evolved out of engagement with community stakeholders and expert knowledge of the socioecological landscape. Thus, the Total Community Vulnerability Assessment Framework (TCVAF) provides an effective decision support for identifying communities’ vulnerability status and help to design both short and long term interventions options that are community specific as a way of enhancing their coping and adaptive capacity to 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".