Smart City as Urban Innovation: A Case of Riyadh North-West District
Bibliographic record
Abstract
Saudi Arabia has recently adopted and implemented an enduring strategy of development that shifts its focal point towards the formulation of knowledge based society. In the same context, KSU (King Saud University), in Riyadh has initiated the project of the Riyadh Techno Valley (RTV), within its campus. The projects aims to strengthen its efforts, in order to develop knowledge based society in Saudi Arabia. KSU as a core of the North-West district of Riyadh had an initiative by adopting a comprehensive idea of Riyadh Knowledge Corridor (RKC) in Prince Turki Alawal Road area. KSU has started to take an active role in setting up new Riyadh’s Smart City node (Smart Riyadh – NWD). The focal point of it is the Riyadh Techno Valley (RTV) project, which is expected to play a central anchored role with other developments in this district such as King Abdul Aziz City for Science and Technology, information technology and communication complex, Saudi Standards, King-Abdulaziz-and-his-Companions-foundation-for-Giftedness-and-Creativity, and king Abdullah financial center. This research study will assesses Riyadh Techno Valley, which will help in reflecting several issues and principals towards the evolution of the North-West district in Riyadh as Smart city.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".