An Overview of the Shipbuilding Labour Market: The LeaderSHIP EU Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it