Bibliographic record
Abstract
This paper explores the relationship between sea ice conditions and cruise tourism activities in the Arctic Archipelago of Svalbard. It analyzes how cruise tourism planning and organization depend on sea ice conditions and to what extent Arctic climate change influences tourism. A mixed-method approach, including sea ice analysis and interviews with 13 cruise tourism stakeholders, was applied to grasp the complexity of Svalbard’s cruise tourism. The outcomes show that cruise traffic depends on the sea ice cover, but only to some extent. Other factors, such as a location’s attractiveness or sailing regulations also influence cruise itineraries around Svalbard. Sea ice conditions are in general considered favorable for cruising around Svalbard, and sea ice is a not a decisive factor in cruise planning and organization. The sea ice cover around Svalbard is decreasing; thus, high annual and inter-annual unpredictability of sea ice poses challenges for cruise operations around Svalbard. Flexibility in itineraries, plus good cooperation and management help the cruise industry adjust to any challenges arising from uncertain sea ice conditions. However, issues of overcrowding and decreased attractiveness due to disappearing ice are more and more visible and may challenge the development of cruise tourism around Svalbard in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".