Is Demonstrating the Concept of Multi-Use Too Soon for the North Sea? Barriers and Opportunities from a Stakeholder Perspective
Bibliographic record
Abstract
Multi-use (MU) has been promoted as a viable approach to the effective planning and mitigation of user-conflicts in the marine realm. Despite several research and pilot projects demonstrating the approach’s feasibility and benefits, commercially viable MU applications remain patchy and few. Further, MU is neither systematically applied nor purposively planned for even in the imminent event of incompatible and conflicting use of marine space. This paper seeks to identify barriers and opportunities for mainstreaming MU based on desktop study and iterative stakeholder consultation. The findings reveal that the MU concept was frequently framed as ‘co-location’ or ‘co-existence’ and aimed toward mitigating conflict among users. Practice was ahead of theory with little attention to synergistic and efficiency aspects. Barriers for MU application include shortcomings in legislation, sectoral thinking, and burdensome administrative procedures. The main opportunity lies in creating a conducive policy environment where MU risks and transaction costs become low and competitive, respectively. Solutions at the sea basin and national level, upon which further MU application can be anchored, are proposed.
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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 | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".