Bibliographic record
Abstract
The ability of tourism regions to attract tourists depends to a great extent on the position of these destinations in the minds of key travel markets. The projection of an appropriate image has been described as a vital element in the positioning process. This research examines the evolving character of wine tourism destination imagery as projected by wine producers and independent writers. The overriding research questions addressed in this paper are “What destination attributes are emphasised in the visual imagery of wine tourism regions, and how has the emphasis on those features varied over time?” The findings suggest that there has been a shift in wine country imagery from an emphasis on wine production processes and related facilities to move of a focus on aesthetic and experiential values associated with more leisurely recreational and tourist pursuits. Over the past decade, the wine tourism experience has become more positioned around the core attraction of a quality wine, accompanied by a set of natural landscape, culinary, educational, event hosting and cultural dimensions. The research identifies the need for a greater emphasis to be placed by wine tourism destinations on protecting rural landscapes, encouraging authentic and unique forms of development, and focusing imagery projection on those elements of the wine country experience which are central to the interests of wine tourists.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".