Kesiapan Aktor dan Kebijakan dalam Mewujudkan Smart Mobility di Provinsi Bali
Bibliographic record
Abstract
Purpose: As the gateway to Indonesian tourism, Bali needs to transform into a smart city to overcome the complexity of the political environment, social and economic disparities, resolve rigid administrative systems, and increase the effectiveness of city infrastructure. One of the infrastructure problems is in the transportation sector which is caused by limited public transportation facilities to keep up with the increasing use of land as a generator and the use of private vehicles. This research tries to analyze the role of the actors and policies involved and the role of policy, power, interests, the relationship between actors and policies in realizing smart mobility. Research methods: Data sources for analysis were obtained from secondary data from planning documents and were verified through limited discussions with stakeholders. Results and discussion: The results obtained by policies related to smart mobility in Bali Province have fulfilled all the components that form smart mobility in Bali Province. Actors with high capabilities and interests include the Inna Group, Electric Vehicle Committee, Transportation Agency, PLN, and GIZ. The analysis of the relationship between policies, actors, and indicators of smart mobility shows that all actors and policies in Bali Province are suitable for realizing smart mobility. Implication: By recognizing smart mobility, it is hoped that people will get a better quality of life in several aspects: a better environment, better public services, and better economic and employment opportunities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".