Evaluating prioritization of ASEAN highway network development using a fuzzy multiple attribute decision making method
Bibliographic record
Abstract
Abstract The association of South East Asian Nations (ASEAN) has recently decided to develop a new highway network to connect countries in the association by roads to enhance cultural integration and economic growth of Asian countries. A total of 5400 km of roads are required to be newly constructed or upgraded to develop the ASEAN highway network. In this study, prioritization of investment for 32 road sections in the ASEAN highway network construction was evaluated using a two‐step hierarchical fuzzy multiple attribute decision‐making (MADM) process. The first step was to prioritize each corridor, and the second step was to prioritize individual roads within each corridor. In this prioritization, the importance of road sections was evaluated based on economic and political aspects. The economic aspect was assessed using traffic volume and construction cost while the political aspect was assessed using data related to balancing regional development, a country's willingness to invest, road network connectivity, and priority of each corridor. The road sections from Mawlamyine to Thanbyuzayat were identified as the highest priority road sections for construction. Also, it was found that the application of the Fuzzy MADM process for evaluating the prioritization of road construction is a useful and beneficial method in prioritizing highway development across various regions such as Southeast Asian countries. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".