Sustainable Development in Smart and Resilient Local Government Areas: An Empirical Investigation of Victoria, Australia
Bibliographic record
Abstract
Experts always seek for improving the development and management of multidimensional urban systems, including those of sustainability, smartness, and resiliency. These dimensions are the main keywords in related research to model and predict better development in urban and regional areas; there are overlapped concepts, common attributes, and parallel processes in existing indices designed for each of those keywords, which might not be an ideal option for the status quo. Therefore, there is a need to find a balance between these concepts/indices and identify an integrated development strategy that addresses smart, resilient, and sustainable development demands. For this purpose, first of all, attributes and themes used to develop the development indices are collected from the recent literature. Secondly, a semantic text mining technique is used to discover commonly used attributes among the collected ones. Thirdly, Principal Component Analysis (PCA) is used to investigate the correlation between the selected attributes to reduce or merge similar attributes. Fourthly, after collecting data and normalizing calculated scores for each LGA, a k-means clustering method is used to identify LGAs with similar development behaviour. And finally, the developed index is implemented in Victoria, Australia as a case study that includes 79 regional and urban local government areas. Evaluation of the results (comparing the results with two existing studies) indicated the success of the proposed index in bringing smartness, resiliency and sustainability indices under a united and comprehensive development index.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".