Multi-Criteria Decision Making of Sustainable Adaptive Reuse of Heritage Buildings Based on the A'WOT Analysis: A Case Study of Cordahi Complex, Alexandria, Egypt
Bibliographic record
Abstract
This study presents a multi-criteria strategic approach of decision-making in sustainable adaptive reuse by evaluating cultural heritage assets and identifying potential alternatives. For effective preservation, adaptive reuse of heritage buildings is a strategic decision. Whereas adaptive reuse decisions are based on several, sometimes contradictory criteria, in addition to decisions from multiple parties and stakeholders are potentially inconsistent. This research finds that the reuse process should consider many important criteria to expand and enhance the knowledge base. This paper presents a systematic application and analytical method in decision-making for adaptive reuse of heritage Cordahi complex in Alexandria, Egypt. The A'WOT analysis application was used as an analytical tool to obtain results through the integration of a SWOT matrix and an Analytical Hierarchy (AHP) process. The SWOT technique was used to examine the internal and external factors and identify the important strategic factors, then apply the AHP method to prioritize these factors to make them measurable. Then, SWOT priority factors were used to formulate strategies using the TOWS Matrix. The proposed strategy relates to protecting and promoting the importance of heritage and the context, enhance the tourism potential, economic development for the population, interpretation strategy, community engagement, sustainable management, partnerships.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| 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".