Integrated Spherical Decision-Making Model for Managing Climate Change Risks in Africa
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
Decision-makers, researchers, practitioners, and stakeholders often struggle with selecting and prioritizing strategies to manage climate change risks. While recent research extensively explores this issue, the emphasis has largely been on regions other than Africa. This is significant, considering Africa’s anticipated exposure to various and severe impacts of climate change. This study applied a two-stage model that integrates the Step-Wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods within a unique framework under the influence of spherical fuzzy (SF) conditions. In the initial stage, SF-SWARA determines the relative importance of the criteria, while the subsequent stage involves the SF-WASPAS method to rank the strategies. While the most critical challenges are limited access to finance and inadequacies in climate data, scenarios, and impact models, the solution to be considered is the promotion of a well-coordinated capacity-building programme. Furthermore, a comprehensive sensitivity analysis was conducted to validate the applicability of the proposed model. This research not only identifies and explains the challenges associated with climate change risks management in the African context but also significantly contributes to the body of knowledge by outlining and prioritizing the strategies required to address these challenges.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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