The implementation of minimum service standards on ship operational performance: Empirical evidence from Indonesia
Bibliographic record
Abstract
The purpose of this study was to determine the effect of ship worthiness and seafarers' skills on improving ship operational performance through minimum service standards. Some early indications found in the field, such as the low performance of ship maintenance by procedures, and the obstacles in implementing the ship maintenance plan due to the inefficiency of the ship's operating schedule. This study uses Path Analysis with Path Coefficients Sub Structure 1 and Sub Structure 2. The test method used for the direct effect is the regression method while the indirect effect is the Sobel test. The number of samples is 80 respondents from national shipping companies, from various types of ships, namely, crew boats, patrol boats, utility boats, supply vessels, and anchor handling towing supply. The results of the study indicate that there is a positive and significant effect of seaworthiness and seafarers' skills on increasing ship operational performance, directly or indirectly through minimum service standards. The minimum service standard variable can mediate the indirect effect of the seafarers' skill and ship worthiness variables on the operational performance of the ship. To meet the growing customer satisfaction as the consequences of tight competition, it is necessary to improve the quality of the fleet where durability, cleanliness of the fleet, and safety facilities in the fleet are expected to provide a high guarantee for 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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".