Sustainable Culinary Systems. Local Foods, Innovation, and Tourism & Hospitality.
Bibliographic record
Abstract
Part 1: Introductory Context 1. Sustainable Culinary Systems: An Introduction Part 2: Reinforcing the in Food and Tourism 2. Culinary Networks and Rural Tourism Development: Constructing the through Everyday Practices 3. Real Food in the US: Food Initiatives, Government and Tourism 4. Rorosmat: The Development and Success of a Food Brand in Norway 5. The in Farmers Markets in New Zealand 6. Is Local Just a Hot Menu Trend? Exploring Restaurant Patrons' Menu Choices when Encountering Food Options 7. Accessing the Effects of Brand Farm Products on the Linkage with Tourism: Evidence from Japan 8. The Evolving Relationship Between Food and Tourism: A Case Study of Devon in the 20th Century 9. Raising Awareness of Food Through Tourism as Sustainable Development: Lessons from Japan and Canada Part 3: Slow and Sustainable Food and Tourism 10. Slow Baltic: The Slow Food Concept in Relation to Baltic Gastronomy 11. Collaboration in Food Tourism: Developing Cross-Industry Partnerships 12. Sustainable Winegrowing in New Zealand 13. Regulatory and Institutional Barriers to New Business Development: The Case of Swedish Wine Tourism 14. Sustaining Halal Certification at Restaurants in Malaysia 15. Heritage and Authenticity in Food Tourism Part 4: Conclusion 16. Conclusion: Re-imagining Sustainable Culinary Systems
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".