Sustainable Indicators Framework for Strategic Urban Development: A Case Study of Abu Teeg City in Assiut, Egypt
Bibliographic record
Abstract
A fruitful approach to the sustainability of cities is described through criteria and indicators that measure the effectiveness of urban operations. To create a sustainable urban environment, an effective strategic approach must be developed that achieves sustainability criteria and indicators. It is difficult to evaluate sustainability strategies due to a sheer number of indicators or the heterogeneity of evaluating options and their relevance. In this context, the research is concerned with reviewing the previous literature for sustainable standards and indicators by specialized international organizations. For achieving a proposed structure of indicators that is easy to implement and includes the essential aspects of sustainability to serve as a priority setting support to propose a sustainable strategic approach. The analytical approach was used in the current situation of Abu Teeg City in Assiut using SWOT technology to examine the internal and external factors through questionnaires and discussions with experts, then applying the AHP method to prioritize the factors to make them measurable. Hence, an effective approach to city sustainability can be discussed using TOWS matrix. The proposed approach relates to the promotion of heritage tourism, infrastructure, environmental development, agriculture, economic and social development of the population, sustainable management, and the housing sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".