Legal mechanisms for sharing the benefits of cruise tourism with indigenous peoples in the Canadian Arctic and Greenland
Bibliographic record
Abstract
The rapid expansion of Arctic cruise tourism, driven by better access to remote regions due to climate change and the allure of "last chance" tourism, has intensified cruise activity along the Northwest Passage, raising concerns about its impacts on Indigenous communities in Canada and Greenland. While benefit sharing is a well-established concept in the extractive sector, its application in cruise tourism remains limited due to the industry’s transient and non-extractive nature. This research examines existing international instruments and domestic legal and non-legal mechanisms, as well as the perspectives of Indigenous communities, to assess how benefit sharing is conceptualised and implemented in the Canadian Arctic and Greenland. It also applies the concept of Social License to Operate (SLO) to evaluate how communities’ perspectives on cruise tourism development influence the governance of this industry and practices exercised by cruise operators. Findings reveal that in the Canadian Arctic, benefit-sharing frameworks are grounded in treaty rights and government programs but lack cruise-specific regulation. In contrast, Greenland has begun to integrate community concerns into legislation, aiming to enhance local ownership and control over the revenues generated by the industry. However, both regions face gaps in enforcement, capacity, and formal mechanisms to hold cruise operators accountable. Indigenous communities increasingly make efforts to guide cruise tourism through management plans and semi-formal partnerships, yet these rely more on goodwill than binding commitments. The Association of Arctic Expedition Cruise Operators (AECO) plays a key role in facilitating dialogue between Indigenous communities and cruise operators, promoting community engagement and best practices among its members. This thesis argues that cruise tourism in the Arctic requires legal frameworks tailored to the sector, grounded in Indigenous Peoples’ rights and SLO principles. Strengthening Indigenous participation and formalised partnerships are essential to ensure benefit sharing that supports sustainable development of the industry and advances Indigenous empowerment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".