Evaluate Ho Chi Minh City Sustainability Using Fuzzy Extent Analysis Method
Bibliographic record
Abstract
Sustainable development of cities was among the goals aimed by either country or region since the 1980s. Ho Chi Minh City was ranked as the most rapid urban development in Vietnam, which challenged the accommodation of the necessities for a pleasant life in a city with limited resources, including housing, public infrastructure, a clean environment, security, safety, employment, and other necessities. The purpose of this study was to measure city sustainability by employing fuzzy decision analysis. A systematic review of the literature has provided the theoretical framework for measuring sustainable cities. Further consent on the criteria of a sustainable city in the context of Ho Chi Minh City, Vietnam was confirmed based on the evaluation of thirty experts with academic and practical experience in the field. The research findings provided the measurement model of city sustainability at three levels with three main criteria at 2nd level and twenty sub-criteria at 3rd level. The research results revealed that there is great consent for city performance and priority ranking in terms of the social dimension. However, great conflict in the importance and performance of economic and environmental dimensions has been found. This practically implied the strategies for bridging the gap between the city’s actual criteria performance and priority ranking in target city sustainability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".