A comparative analysis of regulatory instruments for managing marine-based tourism in Arctic Canada and the Ross Sea
Bibliographic record
Abstract
Human activities, including tourism, in the Polar Regions are increasing and diversifying. The most common mode of transport in support of polar tourism is by cruise ship. At the same time, we are witnessing a rapid increase in the number of small vessels, such as yachts, exploring the Polar Regions. From a political and legal perspective, operating cruises to the Arctic and Antarctic is highly complex. In order to better understand whether the current regulatory mechanisms are sufficient for the rapidly changing nature of Polar marine tourism activities, this poster presents the results of a desk-based analysis of the regulatory landscape in two contrasting case studies. Our first case study focuses on Arctic Canada, where significant regulatory complexity currently represents significant barriers to entry for new tourism operators. The second case study we explore is marine tourism to the Ross Sea region, where a short season and the destinations remoteness limit the number of operators. Tourism here, and across the entire Antarctic region, is subject to high-level regulation under the Antarctic Treaty System as enacted by national jurisdictions. The interplay of international regulation through, e.g., the IMOs Polar Code or UNCLOS, with national policies is the focus of our examination.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".