Tourism and the consumption of wildlife : hunting, shooting and sport fishing
Bibliographic record
Abstract
1. An Introduction to Consumptive Wildlife Tourism 2. The 'Animal Question' and the 'Consumption' of Wildlife 3. The Lure of Fly-Fishing 4. The Scandinavian Sporting Tour 1830-1914 5. Controversies Surrounding the Ban of Wildlife Hunting in Kenya: A Historical Perspective 6. Game Estates and Guided Hunts: Two Perspectives on the Hunting of Red Deer 7. Shooting Tigers as Leisure in Colonial India 8. Conservation Hunting Concepts, Canada's Inuit and Polar Bear Hunting 9. Environmental Values of Consumptive and Non-Consumptive Marine Tourists 10. The Success and Sustainability of Consumptive Wildlife Tourism in Africa 11. Trophy Hunting and Recreational Angling in Namibia: An Economic, Social and Environmental Comparison 12. Welfare Foundations for Efficient Management of Wildlife and Fish Resources for Recreational Use in Sweden 13. What Happens in a Swedish Rural Community When the Local Moose Hunt Meets Hunting Tourism? 14. Arab Falconry: Changes, Challenges and Conservation Opportunities of an Ancient Art 15. Communicating for Wildlife Management or Hunting Tourism: The Case of the Manitoba Spring Bear Hunt 16. Catch and Release Tourism: Community, Culture and Consumptive Wildlife Tourism Strategies in Rural Idaho 17. Marine Fishing Tourism in Lofoten, Northern Norway: The Management of the Fish Resources 18. Footprints in the Sand: Encounter Norms for Backcountry River Trout Anglers in New Zealand 19. Australia as a Safari Hunting Destination for Exotic Animals 20. Conclusion: Consumptive Wildlife Tourism - Sustainable Niche or Endangered Species?
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".