Nature-based Tourism in Peripheral Areas: Development or Disaster?
Bibliographic record
Abstract
1. Introduction: Nature-based Tourism and Regional Development in Peripheral Areas - C. Michael Hall & Stephen Boyd Section one: Nature-based Tourism in Alpine, Forest and Sub-Polar Environments 2. Ecotourism and the Resource Hinterland in Northern Ontario - Margaret Johnston & Robert Payne (Lakehead University) 3. Wilderness Discourses and the Development of Nature-Based Tourism in Northern Finland - Jarkko Saarinen (Oulu University) 4. Mountain Scenic Flights A Low Risk, Low Impact Ecotourism Experience Within South Island, New Zealand - Nicholas J Westwood (University of Otago) & Stephen W Boyd 5. Regional Contrasts in Ecotourism in Rainforests - Warwick Frost (Monash University) 6. Destination Image: Victorian Mountain Parks and Resorts in the Summer - Roslyn Russell (RMIT University) & Philippa Thomas (La Trobe University) 7. Winter Tourism in Protected Pleasure Peripheries: Time-Space Use Among Cross-Country Skiers in Abisko (Sweden) and Vercor (France) -Ludovic Dupuis & Dieter K. Muller (Umea University) 8. The importance of health as a factor in achieving sustainability in a high altitude destination of a less developed country - Ghazali Musa (University Malay) 9. Second Home Tourism in the Swedish Mountain Range - Dieter K. Muller (Umea UniversitySection two: Island, Coastal and Marine Environments 10. Pest or Precious Tourism Commodity? The Economic Value of White-Tailed Deer Hunting on Stewart Island / Rakiura - Brent Lovelock & Kevin Robinson (University of Otago) 11. The Economic Benefits of an Ecotourism Project in a Regional Economy - Filipo Tokalau (University of the South Pacific) 12. Growth of Beach Fale Tourism in Samoa: The High Value of Low-Cost Tourism - Regina Scheyvens (Massey University) 13: Doing it right the first time? Ecotourism on the Wild Coast of South Africa - Dorothy Queiros (University of Hertfordshire) & G.D.H. Wilson (University of Pretoria) 14: Penguins as Sights - Penguins as Site: The Problematics of Contestation - Eric J. Shelton (University of Otago) & Hildegard Lubcke (Nature Guides Otago) 15. Dolphins, Whales and Ecotourism In New Zealand: What are the Impacts and how Should the Industry be Managed? - Mark Orams (Massey University) 16. The state of the scenic cruise industry in Doubtful Sound in relation to a key natural resource: bottlenose dolphins - David Lusseau (University of Aberdeen)Section three: Nature-based Tourism in Peripheral Areas: A Tool for Regional development? 17. Ecotourism/Egotourism and Development - Brian Wheeler (Breda University) 18. Nature-based tourism in Peripheral Areas: Making Peripheral Destinations Competitive - Stephen Boyd and C. Michael Hall
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".