ccessing the Sustainable Potential of Urban Projects Towards Smart Cities in Japan and the Middle East
Bibliographic record
Abstract
Today’s new perspectives on ‘smart’ cities are becoming increasingly different from the past when people thought about the idea of urbanism which was the central terminology for studying the changing urban environment. With the rapid development of cities, the definitions and functions of urbanization are shifting towards green-urban development and sustainable ‘smart’ innovations. These changes are because of globalization, goals of a green future, and inter-cooperation locally and globally. This paper examines the sustainable potential of urban projects towards ‘smart’ cities in Fukuoka, Japan, and Dubai, the Middle East, with the two different cases and follows discussion on the aspects of ‘smart’ and ‘greening’. This article incorporates a fourfold methodology. First, the conceptualization of ‘urbanism’ is explored by examining the previously researched accomplishments and terminologies about urban studies and smart cities. Secondly, the Japanese case study on the city of Fukuoka is analyzed by considering Fukuoka is a great example since it innovates and practices on the built environment with the thinking of sustainability and the geographic feature of Japan where it has been reported that Japan is in a high threat level of natural disasters. Thirdly, the Dubai case study is argued by criticizing how the urban projects serve and improve the city in the ‘smart’ and ‘greening’ way and paying attention to the real estate and man-made environment. Fourth, this paper is concluded with a discussion to extract the points of smart cities and sustainable development from the two cases. In summary, the applicability of the sustainable potential of urban projects in Fukuoka and Dubai may provide real-life examples for reviewing and studying. The future operations in urban development and the innovative constructions in smart cities would be case by case based on the individual circumstances of different cities worldwide to react and manage the influences and changes caused by an unpredictable climate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".