The Role of Placemaking Approach in Revitalising AL-ULA Heritage Site: Linkage and Access as Key Factors
Bibliographic record
Abstract
Tourists visiting urban heritage sites are guided by constructed itineraries that are shaped by a coherent spatial structure.This article studies the shaping of these itineraries in the heritage city of Al-Ula, using space syntax technique.The main hypothesis is that a placemaking approach of a destination based on its specificities will be able to contribute to the tourism development and place attractiveness.Building on previously developed place-making models, this study focuses on how a well-accessed and linked heritage site can be modelled and evaluated via Space Syntax measures to improve the quality of tourists' spatial experience.As the use of space syntax was found to be a useful technique for analysing tourists' itineraries and the factors that affect it; This study uses it to suggest the most appropriate pedestrian walkway given the various constraints of the existing historic fabric and to identify multiple measures of a destination's assessment.These measures have been noted in the previously developed models.The adopted measures that address access & linkage parameters include connectivity, integration, choice, and legibility.The findings explain that the adoption of access & linkage parameters contribute to revitalising the place in the sense of connecting the entire heritage site and enhancing the visitors experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".