Future policies for managing ship traffic and safety in the access channel of a new nation’s capital: A case study of Indonesia
Bibliographic record
Abstract
Indonesia has a new capital, officially known as Nusantara (IKN). The nearest access to and from IKN is through Balikpapan Bay, a confined waterway that may eventually result in traffic congestion and interfere with shipping operations. This research aims to investigate ship traffic and safety governance policies, as few researchers have previously studied this issue in the IKN waters. We collected the empirical data in four steps. Firstly, we conducted in-depth interviews and focus group discussions attended by related stakeholders, such as the Harbormaster and Port Authority, the Indonesian Maritime Court, the Navigation District Officers, SOE Port Managers, Local Government, and Shipping Companies Association. Next, we distributed questionnaires to shipping operators. Furthermore, using triangulation techniques, this research suggests the need to harmonize regulations implemented by related agencies involved in shipping activities. The last step was determining ship routes to ensure maritime safety and ship traffic efficiency. The proposed harmonization would provide port and shipping operators with business certainty in accordance with applicable laws. The research also recommended sharing authority between the IKN Authority Agency, which is responsible for the exploitation of water areas, and the Transportation Ministry, which regulates maritime traffic and safety.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".