Tourism destination image (TDI) perception of a Canadian regional winescape: a free-text macro approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it