A virtual geobibliography of polar tourism and climate change
Bibliographic record
Abstract
The polar regions are increasingly at the center of attention as the hot spots of climate crisis as well as tourism development. The recent IPCC reports highlight several climate change risks for the rather carbon-intensive and weather-based/dependent polar tourism industry in the Arctic and the Antarctic. This study presents the scholarly state-of-knowledge on tourism and climate change in the polar regions with a literature survey extending beyond the Anglophone publications. As a supporting tool, we provide a live web GIS application based on the geographical coverages of the publications and filterable by various spatial, thematic and bibliographical attributes. The final list of 137 publications indicates that, regionally, the Arctic has been covered more than the Antarctic, whilst an uneven distribution within the Arctic also exists. In terms of the climate change risks themes, climate risk research, i.e. impact and adaptation studies, strongly outnumbers the carbon risk studies especially in the Arctic context, and, despite a balance between the two main risk themes, climate risk research in the Antarctic proves itself outdated. Accordingly, the review ends with a research agenda based on these spatial and thematic gaps and their detailed breakdowns.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".