The impact of the safety of passenger ship services on the development of water recreation: evidence from Indonesia
Bibliographic record
Abstract
This research aims to assess the safety of ship passenger services as one of the efforts to promote water recreation on the Musi River, South Sumatera. Rivers in Indonesia, through their role as a means of moving goods and people, greatly influence transportation and tourism, especially water recreation. Without safe transportation, there will be no travel and tourism industry. The accident rate of river transportation in Indonesia, including on the Musi River, today is still relatively high, and there still is no care about the assessment of riverboat services safety. The safety assessment was done with the analysis method using gap analysis with the analysis technique of Importance Performance Analysis. The research was conducted in Wharf 16 Ilir Palembang with a sample of as many as 264 people, including ship operators, passengers, and regulators. The study finds first that most users of riverboat services on the Musi River tend to be unsatisfied. Second, the need to improve the information on the safety equipment storage and the availability of safety equipment use instructions. Based on the findings, to develop water recreation on the Musi River, boat condition setting, and boat passenger safety are essential factors to be prioritized since the passengers or tourists mainly consider them in using sea transportation.
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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 | 0.004 |
| 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 | 0.001 | 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".