Tourism destination image (TDI) perception of a Canadian regional winescape: a free-text macro approach
Bibliographic record
Abstract
This research conceptualises a wine region destination’s perceived image by integrating servicescape and destination choice theory using a ‘back-to-basics’ free-text macro approach. The study (n = 510 respondents) outlines the process of conceptualising a wine region destination’s image in the form of a winescape framework as it is perceived by tourists. The winescape construct is identified within a framework of nine dimensions for a Canadian wine region. The most important winescape dimension is the destination’s natural beauty/geographical setting of its landscape. The first-time and repeat visit dynamic impacts differently on visitors’ perception of the destination region’s winescape. For wine tourism ‘specialists’ and wine tourism ‘generalists’ there are pronounced differences in their perception of the region’s winescape dimensions. The decision to engage in wine tourism, even while primarily on vacation, is seemingly impulsive from a timing viewpoint and the motivations guiding the visitors’ behaviour are of a fairly hedonic nature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".