Consuming Location: The Sustainable Impact of Transformational Experiential Culinary and Wine Tourism in Chianti Italy
Bibliographic record
Abstract
Tourists visit wine and culinary destinations for unique, geographically indicated experiences that are place specific. The objective of this research is to understand how the transformational potential of experiential wine and culinary tourism best promotes sustainability in the context of international educational travel. Our case study in the iconic Chianti Region of Italy applies a ‘Hopeful Tourism Enquiry’ perspective and focuses on participatory, co-transformative learning, and mindful sustainability. A mixed qualitative research strategy was implemented that integrates the results of in-depth interviews with industry experts, excerpts from expository travel journals simultaneously captured during the experience, and focus group dialogues with participating students at the end of the field course. This case study revealed three overlapping thematic results that illustrate the influence of experiential educational tourism on the sensory and cultural experience of sustainable food and wine to produce co-transformative learning. The co-creation of memorable experiences establishes a unique sensual representation of provenance through the interaction with the region through narrative so that not only is the food and wine being consumed, but also the consumption of place through the storyscape of a positive and memorable experience.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".