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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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