An Overview of the Shipbuilding Labour Market: The LeaderSHIP EU Project
Bibliographic record
Abstract
In recent years, the maritime technology and shipbuilding industry, including yachts and pleasure crafts as well as marine structures, have faced significant challenges, such as technological advancements, environmental sustainability, and global competitiveness. This article presents a comprehensive investigation conducted within the European project LeaderSHIP, focusing on the current state of training programs for workers in the sector and specifically identifying emerging and urgent skills necessary for future success. Using a mixed-methods approach, data were collected through surveys distributed to industry professionals, educational institution representatives, and companies. The results highlight a disparity between the skills demanded by employers and those provided by training programs. Notably, the investigation reveals critical gaps in essential areas such as sustainable design, technological innovation, and resource management, underscoring the need for immediate action. The analysis emphasizes the importance of continuous updating and a closer alignment between academia and industry. It suggests that collaboration between educational institutions and businesses could significantly enhance training quality. Implementing practical learning programs and internships, along with creating flexible curricula that can adapt to the sector’s dynamic needs, is proposed as a vital strategy to address these gaps. Finally, the article discusses future perspectives for training in maritime technology, stressing the necessity to invest in advanced skills and foster a culture of innovation. This study provides valuable insights for policymakers, educators, and entrepreneurs, highlighting the critical need for an integrated approach to effectively tackle the challenges posed by emerging and urgent skills in the shipbuilding industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".