Equator to Pole: Using Science Diplomacy to Incorporate Best Practices from Latin America and the Caribbean for Sustainable Cruise Tourism in the Polar Regions
Bibliographic record
Abstract
During the last decade, cruise tourism in the Arctic and the Antarctic has grown exponentially as changing environmental conditions have enabled increased access to the polar regions. This industry has the potential to support communities via financial and social benefits; however, it is also a climate- and capital-intensive global economic activity whose negative impacts are difficult to assess, monitor and control. The current state of voluntary, industry-led standards and regulations around polar cruise tourism are likely insufficient to deal with the negative environmental impacts which will escalate in the coming years due to global warming and over-tourism. As such, science diplomacy—a mode of international relationship-building that mobilizes science, technology, and innovation to tackle transnational issues—can be used as a tool to enhance science-based conservation efforts and address cruise tourism’s governance challenges that often escape jurisdictional boundaries. Addressed to the United Nations World Tourism Organization (UNWTO), this policy memo outlines how science diplomacy can help develop collaborative policies to manage the risks and opportunities of cruise tourism expansion in the polar regions. It presents key recommendations based on best practices from regional experiences in Latin America and the Caribbean, including convening an international workshop on climate change and cruise tourism, soliciting a request for proposals for establishing a UNWTO polar observatory, and using outreach tools to develop knowledge-sharing partnerships.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".