Tourism in peripheries: perspectives from the far north and south
Bibliographic record
Abstract
* Preface Part I: Tourism in Peripheries - An Introduction * The Difficult Business of Making Pleasure Peripheries Prosperous: Perspectives on Space, Place and Environment, D K Muller and B Jansson * North-South Perspectives on Tourism, Regional Development and Peripheral Areas, C M Hall, University of Otago, New Zealand Part II: Tourism and Regional Development Issues * Tourism in Peripheries: The Role of Tourism in Regional Development in Northern Finland, J Saarinen, University of Oulu, Finland * Organising Tourism Development in Peripheral Areas: The Case of the Case of the SUBARCTIC Network in Northern Sweden, M Zillinger, Umea University, Sweden * The Impact of Tourism on the Local Structure of Supply with Goods and Services in Peripheral Areas - The Example of Northern Sweden, G Loffler * Tourism Development and the Rural Labor Market in Sweden, 1960-1999, D K Muller and P Ulrich, Umea University, Sweden Part III: Challenges to Peripheral Area Tourism * The Vulnerability of Peripheral Tourism: The Rapid Disenchantment of Peripheral Attraction, W Irvine and A R Anderson, Robert Gordon University, UK * If that's a Moose, I'd Hate to See a Rat! Visitors' Perspectives on Naturalness and their Consequences for Ecological Integrity in Peripheral Natural Areas of New Zealand, B Lovelock, University of Otago, New Zealand * Access, Tourism and Democracy: A Conceptual Framework and the Non-Establishment of a Proposed National Park in Sweden, K Sandell, Karlstad University, Sweden * Visitor Management in Protected Areas of the Periphery: Experiences from Both Ends of the World, P Mason, University of Luton, UK Part IV: Tourism Opportunities * Wind Farms as Possible Tourist Attractions, R Nash, A Martin, D Carney and K Krishnan, Robert Gordon University, UK * Sport Events as Tourist Attractions in Canada's Northern Periphery, T Hinch and S de la Barre, University of de la Barre Part V: Future Perspectives * Tourism Research in Greenland, B C Kaae, Danish Center for Forest, Landscaping and Planning, Denmark * Epiloge/Prologue, B Jansson and D K Muller.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".