Charting New Waters: Adoption of AI across Dutch and Danish Ports
Bibliographic record
Abstract
The adoption of artificial intelligence (AI) in ports varies as a result of divergent value propositions of these advanced technologies. The smart port paradigm as the new way of business is both embraced and rejected. Ports regularly hold back technological advances due to concerns about potential disruptions to existing processes. The adoption of AI destabilizes routine practices in port operations and logistics, creating a discontinuity, thus undermining their sense of Self as a conventional port. Barriers to organizational change are thus fundamentally anchored in a port’s ability to maintain ontological security. In this paper, we seek to advance our understanding of how to reconcile organizational change while maintaining a sense of stability, or continuity, particularly focusing on the adoption of AI across Dutch and Danish ports. To address this objective, we combine literature on AI drivers of smart ports in conjunction with ontological security theory. We employ a systematic literature review method and narrative content analysis to examine the port stakeholders’ value propositions of AI to identify patterns and themes regarding organizational change. The preliminary findings presented illuminate key factors influencing stakeholder adoption, and its connection to a port’s ontological security, and it describes the barriers and enablers to (furthering) organizational change. These findings will equip port management in Denmark, the Netherlands, and beyond, with actionable insights to facilitate smoother AI adoption in a rapidly evolving 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".