Should Canada Allow Autonomous Ships in Its Coastal Waters?—International Context and Legal Implications
Bibliographic record
Abstract
Abstract for Scopus Indexing: Autonomous shipping technologies are already being tested on the ocean and are presumably here to stay. This brings a new set of issues into the shipping world currently being discussed by the international community through the International Maritime Organization. The regulatory framework in the works, the Maritime Autonomous Surface Ships ( MASS ) Code, is planned to take effect in 2025 and will be non-mandatory at first, which means States will have some time and latitude to adapt to this new reality. This article aims to reflect on how Canada should position itself on the international scene and argues that the challenges and the risks posed by these new technologies are sufficiently high to question the value of their imminent presence in Canadian waters and perhaps a ban should be considered, at least in the short term. Additionally, it is argued that the true beneficiaries of autonomous technologies will be the manufacturers and not shipowners or consumers, as many expect, and adopting regulations banning MASS in a coastal States’ waters would not be detrimental to the shipping industry in the early phases of the development.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".