Development of Sustainable Urban Railway Service Model Using Micmac-Mactor: A Case Study in Jabodetabek Mega-Region Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
Electric trains (KRL) provide services to residents living in Jabodetabek, one of the world's most significant regions. Although KRL is used daily by about 973,366 residents to carry out their activities, some factors influence its usage. Therefore, this study aims to identify the critical elements that affect train services and the patterns of relationships amongst actors to construct a model for long-term sustainability. This study was carried out using the Micmac and Mactor methods. Micmac is a causal structural matrix that can investigate the relationship between parameters in a system. The Mactor technique, on the other hand, is applied to a variety of tactics involving many actors and a set of related interests and goals. The results showed five critical variables for sustainable urban rail service, namely Safety, Capital, Eco monitoring and evaluation, Eco plan, and COVID control are needed. Meanwhile, The General Administration of Railways, Ministry of Transport, and Indonesian commuter train company are two institutions or actors that are very influential in mobilizing the safety of KRL users amid a pandemic to ensure the continuity of train services. This study also finds that critical variables, key actors, and rail destinations strongly influence the sustainability of social, economic, and environmental aspects of urban rail transportation services. In conclusion, this study provides new insight into developing a sustainable urban rail service model in Jabodetabek KRL.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it