Industry 4.0 in shipping: Implications to seafarers' skills and training
Bibliographic record
Abstract
Industry 4.0 entails the modernisation of work, which is likely to have an impact on individuals’ employment, training and skills in the foreseeable future. Current debates on future skills for maritime operations tend to focus on technology as a necessary requirement for workers to adapt to changes. This technology-centred approach can be controversial as technology cannot govern how humans work and how they choose their careers after graduating. This paper employs a career-focused perspective that addresses Industry 4.0 and digitalisation from individuals’ career development viewpoint, and discusses the potential implications of digitalisation and automation on individuals’ careers in the maritime industry. The paper contributes to the discussion of how Industry 4.0 and digitalisation have the potential to affect individuals’ skills and training, as well as their future career trajectories. The paper also scrutinises career structures for seafarers as well as possible socio-economic implications on future maritime careers, skills and training in the context of Industry 4.0. These issues are examined through the use of interview data from two empirical projects between 2007 and 2018 as well as a literature review on careers in the global labour market and on Industry 4.0. It concludes with a set of agendas highlighting potential shortage of career support systems for seafarers as well as the need for stakeholder engagement in shaping future maritime skills.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".