Balancing Heritage Preservation and Sustainable Development in Historic Cities: A Case Study of Old Najaf in the Context of Global Best Practices
Bibliographic record
Abstract
Historic cities such as Old Najaf are challenged with balancing the preservation of their cultural and architectural heritage against the pressures of modern urban development.Being one of the major religious centers for Shia Islam, Old Najaf receives annual millions of pilgrims and tourists, pressuring its infrastructure and threatening its historic faç ade.The aim of this study was to explore the viability of integrated approaches that balance heritage conservation with sustainable urban development, and lessons learned from international best practices, as well as stakeholder perspectives on this issue in the face of these challenges.This prompted the study to identify the challenges and opportunities for the heritage preservation and sustainable development in Old Najaf.It aims to comprehend the way residents and experts understand the dynamics of these layers through a mixed methods approach as well as derive actionable lessons learned after comparative analyses with case studies in Fes, Morocco and Kyoto, Japan.The study also explores the role that emerging technologies, regulation, and citizen engagement can play in addressing this trade-off education about Artificial Intelligence.The study shows that the major impediments for Old Najaf heritage are unplanned urban growth, inadequate infrastructure and mass tourism.Survey responses show that both residents and experts prioritize stricter zoning laws at the city's level, improved infrastructure for those corridors and sustainable tourism practices.In Fes, a city already leveraging cutting-edge technologies such as digital twins in urban planning, and building on advances made by Kyoto in GIS, comparative insights from both cities show the need to embed advanced technologies within strong policy frameworks supported by community-led initiatives.The findings underscore the need for holistic and context-sensitive strategies to protect the cultural heritage of Old Najaf, in line with sustainable development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".