Determinants of shipbuilding industry competitive factors and institutional model analysis
Bibliographic record
Abstract
The development of the shipbuilding industry is expected to meet the needs of the Indonesian Navy and the commercial vessels, and to support Indonesia's marine policy. The purpose of this study is to see the Shipbuilding industry Competitiveness, the influence of Technology Transfer to the Shipbuilding industry Competitiveness, and the influence of the industrial clusters on the Shipbuilding industry Competitiveness, as well as to analyze the institutional model of the Shipbuilding industry Competitiveness. This study uses the descriptive analysis, the Structural Equation Modeling (SEM) for the model causality testing, and the Interpretative Structural Modeling (ISM) for the institutional model of the competitiveness of the Shipbuilding Industry. This study uses the primary data, namely a survey of defense industry players, the national industry, the defense equipment users, the government institutions, the research institutes, and the universities that are determined purposively. ISM data are obtained from questionnaires and Forum Group Discussion (FGD) with 13 speakers representing academia, industry, and government. The results of the analysis of SEM state that the indicators on the industrial clusters, the competitiveness, and the technology transfer have a significant and real contribution to these variables. This research also shows that the industrial clusters and the technology transfer have a direct and significant effect on the competitiveness and the industrial clusters directly and significantly affect the technology transfer. However, the industrial clusters also have an indirect effect on competitiveness through the technology transfer to the shipbuilding industry. The results of the analysis of ISM conclude that the stakeholders involved have the greatest driving force, namely the Ministry of Defense and Ministry of State-Owned Enterprises, while the important factor affected by the stakeholders in strengthening the competitiveness of the shipbuilding industry is the Indonesian Navy Headquarters.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".