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
Researchers in culinary tourism often implicitly treat visitors interested in culinary products as a relatively homogeneous market. Using data obtained from the Canadian Travel Activities and Motivations Study, three a priori segments are defined: visitors who participate only in food-related activities, those who participate only in wine- related activities, and those who participate in both. The food segment was the largest of the three, with nearly 25% of respondents fitting this category; wine was the smallest segment with less than 4%. Wine and food accounted for about 7%. The food segment had a higher proportion of females than the other segments, with lower average educational attainment and lower incomes. Wine-oriented visitors were more balanced between male and female, had average ages and educational attainment, and higher incomes. Those visitors involved in both sets of activities were predominantly male, older, had the highest educational levels, and much higher incomes. Trip motivations and activities also differed significantly among the three segments with the food and wine segment showing the greatest diversity of motivations and activities. In other words, there are distinct types of culinary tourists who seek distinct types of culinary experiences. Different methods of communications, and different packaging and product development strategies need to be employed to reach each of the segments identified here.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it