AHP-TOPSIS-Based Evaluation of the Relative Performance of Multiple Neighborhood Renewal Projects: A Case Study in Nanjing, China
Bibliographic record
Abstract
With the rapid development of urbanization worldwide, there is a large volume of neighborhoods that need to be renewed with various problems such as poor building performance, few public facilities, congested road traffic, unequal living standards, disappearing community culture, and deprived environments. Performance evaluations are considered to be useful tools for ensuring the outcomes of sustainable renewal. Although many research works have assessed the performances of urban renewal projects, evaluations, especially for neighborhood renewal projects, are often overlooked. Besides, it is also hard to find a general standard that is suitable for evaluating the performance of any neighborhood renewal project with a lack of related regulations or codes. Thus, this paper intends to build a framework to assess the relative performances of multiple neighborhood renewal projects through a hybrid AHP-TOPSIS method. A case study in Nanjing, China, is used to show how this framework could be applied to decision-making in order to pursue sustainable neighborhood renewal. The results are expected to provide references for sustainable renewal in each neighborhood. Suggestions related to the findings are proposed to further improve the performances of neighborhood renewal projects, such as establishing a multiple principle–agent framework, providing a sustainable funding system from both the public and private sector, and implementing multiprogram management measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".