A Multi-Criteria Decision for Touristic Revitalization of Historic Waterfront Based on AHP Analysis: A Case Study of Ezbet El-Borg City, Damietta, Egypt
Bibliographic record
Abstract
This research finds an approach to support multi-criteria decision-making about the touristic revitalization of the waterfront for the purpose of conservation and sustainable development. The waterfront revitalization strategy is an effective way to preserve the neglected heritage, enhance identity and authenticity, and improve the quality of life. This paper presents a systematic multi-criteria approach and an analytical method in decision-making to revitalize the waterfront of Ezbet El-Borg city, Damietta, Egypt. The waterfront was analyzed according to the criteria of sustainable revitalization. The AHP method was used as an analytical tool to prioritize these criteria to make them measurable, and then suggest an effective strategy for revitalization through the prioritizing alternatives to waterfront functions then used to rank the best prospects for revitalization. The study found the most successful option is to revitalize the historic waterfront of Ezbet El Borg, due to its heritage features. This kind of revitalization plays an essential role in sustainability, as it enhances the city's identity, conservation opportunities, economic development, and quality of life. Applying this approach allows policymakers to develop strategies for waterfront revitalization, and to evaluate the best solutions for the revitalization process with regard to preservation and 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".