Sweden as a last resort for European skiing? An outbound market perspective
Bibliographic record
Abstract
Previous work identified Northern Swedish skiing as a beneficiary from worsening climate change, with its relative improvement in snow reliability increasing its attractiveness over areas of the European Alps. This study advances the supply-side discussion of Northern Sweden as a ‘last resort’ with demand-side insights. It examines whether Europe’s major outbound ski market would adapt its destination choice due to climate change impacts on European skiing. A survey of 296 skiers was administered through the Ski Club of Great Britain. British ski tourists held negative perceptions of the price, accessibility, and quality/variety of ski terrain in Sweden. These concerns improved amongst those who had visited Sweden to ski, demonstrating familiarity with Swedish skiing may overcome barriers to substituting away from the European Alps. British ski tourists ranked snow conditions as the most important factor in their destination choice, thus snow reliability should form the basis of Swedish destination image moving forward. The majority of respondents (76%) opted for spatial substitution under poor snow conditions, ranking Sweden as the fifth most popular substitution destination, after four major Alpine ski nations, indicating that until climate reliable locations in the European Alps are exhausted, Sweden may not benefit substantially from climate change adaptation.
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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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".