Understanding Area-Based Management in Shipping
Bibliographic record
Abstract
Abstract This chapter discusses area-based management (ABM) in shipping in view of developing an understanding of the broad range of tools used and how they are informed by risk and justified by social license. Their purposes are varied and include safety, environmental, security, and public health functions. The chapter first explores shipping-specific and non-shipping-specific ABM tools that have an impact on shipping and proposes an approach to taxonomy and classification. Subsequently, a risk perspective on ABM tools and processes is provided, addressing aspects of risk assessment, management, and governance. Connected especially to the latter, the importance of social license in the context of ABM tools and measures is examined closely. While at first blush the various ABM tools leave an impression of complexity and fragmentation, a closer look demonstrates flexible, nimble, multilevel, and multi-sectoral, problem-solving and management practices operating at the international and domestic levels that inform or guide each other.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".