Competitiveness of Traditional Shipping in Sea Transportation Systems Based on Transport Costs: Evidence from Indonesia
Bibliographic record
Abstract
Traditional shipping vessel is a mode of transportation that is part of Indonesia's cultural heritage and still exists today.However, traditional shipping has been deemed unable to compete with the national shipping fleet due to the high transport cost, the low safety level, long travel times, limited capacity, and limited ship repair facilities.In addition, its existence was eliminated with the advent of modern ships.This study aims to analyse the competitiveness of traditional shipping with national shipping freight based on transportation costs.The analysis used is a gap analysis of transportation costs based on variable costs and fixed costs for every traditional shipping route that overlaps with national shipping.Data were obtained by field observations.The results of the analysis show that the competitiveness of traditional shipping has decreased due to the loss of cargo of national ships.It is necessary to optimize the route by restoring the traditional shipping function as a national shipping feeder, especially in the underdeveloped, remote, outermost, and border (UROaB) areas.This study recommends the integration of the national shipping transportation network as a trunk line and traditional shipping as a feeder line.The shipping integration is expected to form a network pattern and generates increase in the demand for traditional shipping cargo.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".