Tourism and Arctic Observation Systems: exploring the relationships
Bibliographic record
Abstract
The Arctic is affected by global environmental change and also by diverse interests from many economic sectors and industries. Over the last decade, various actors have attempted to explore the options for setting up integrated and comprehensive trans-boundary systems for monitoring and observing these impacts. These Arctic Observation Systems (AOS) contribute to the planning, implementation, monitoring and evaluation of environmental change and responsible social and economic development in the Arctic. The aim of this article is to identify the two-way relationship between AOS and tourism. On the one hand, tourism activities account for diverse changes across a broad spectrum of impact fields. On the other hand, due to its multiple and diverse agents and far-reaching activities, tourism is also well-positioned to collect observational data and participate as an actor in monitoring activities. To accomplish our goals, we provide an inventory of tourism-embedded issues and concerns of interest to AOS from a range of destinations in the circumpolar Arctic region, including Alaska, Arctic Canada, Iceland, Svalbard, the mainland European Arctic and Russia. The article also draws comparisons with the situation in Antarctica. On the basis of a collective analysis provided by members of the International Polar Tourism Research Network from across the polar regions, we conclude that the potential role for tourism in the development and implementation of AOS is significant and has been overlooked.Keywords: Arctic; Antarctic; citizen science; observation systems; tourism; IPTRN(Published: 1 March 2016)Citation: Polar Research 2016, 35, 24980, http://dx.doi.org/10.3402/polar.v35.24980
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".