Assessing the Multidimensional Vulnerability of Lagging Regions: A Case Study of New Valley Governorate Egypt
Bibliographic record
Abstract
In recent years, regional cohesion policies have received increasing attention worldwide. Therefore, international organizations, governments, and researchers have sought to assess the vulnerability in lagging regions. However, most studies have focused on exposure to a particular risk, with little work to examine overall interactions or adaptability from a multidimensional perspective. This study aims to fill the current research gap by developing a multidimensional composite index using the IPCC approach and a Delphi survey to define the criteria and specify the weights of the variables by the AHP method. The results showed that the municipality, which has an adaptive capacity through physical and economic capabilities, has overcome environmental, social, and demographic risks. On the other hand, some municipalities have been affected by their isolation, resulting in a high sensitivity to economic, social, and demographic challenges. A quantitative assessment of the various aspects of vulnerability and adaptability assists experts in mitigating the effects of vulnerability through management, planning, and decision-making; developing appropriate strategies; and serving as a manual for governments in managing vulnerability decrease to achieve sustainable development.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".