Bibliographic record
Abstract
1: The Growing Significance of Polar Tourism, J Snyder and B Stonehouse 2: Pioneers of Polar Tourism and Their Legacy, J Snyder 3: Polar Tourism in Changing Environments, B Stonehouse and J Snyder 4: The Polar Tourism Markets, J Snyder 5: Tourism in Rural Alaska, H Huntingdon, USA, M Freeman, Alaska Department of Fish and Game, USA,B Lucey, Yakutat Salmon Board, USA, G Spearman, Simon Paneak Memorial Museum, USA and A Whiting, USA 6: Development of Tourism in Arctic Canada, M Robbins, Canada 7: The Economic Role of Arctic Tourism, J Snyder 8: Gateway Ports in the Development of Antarctic Tourism, E Bertram, University of London, UK, S Muir, University of Tasmania, Australia, and B Stonehouse 9: Antarctic Shipborne Tourism: An Expanding Industry, E Bertram 10: Antarctic Adventure Tourism and Private Expeditions, M Lamers, J H Stel and B Amelung, all Maastricht University, The Netherlands 11: Antarctic Scenic Overflights, T Bauer, Hong Kong Polytechnic University 12: Antarctic Tourism: What are the Limits? D Landau, IAATO and J Spettoesser, USA 13: Antarctic Tourism Research: The First Half-Century, B Stonehouse and K Crosbie, UK 14: Managing Polar Tourism: Issues and Approaches, J Snyder 15: Tourism in South Georgia: A Case for Multiple Resource Management, J Snyder and B Stonehouse 16: Tourism Management on the Southern Oceanic Islands, P Tracey, Government Antarctic Division, Australia
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.111 | 0.027 |
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".