Understanding the Role of Food in Rural Tourism Development in a Recovering Economy
Bibliographic record
Abstract
Despite its primary, vital function of satisfying physiological needs, food can be the key factor in the tourist industry by adding value to the image of a destination and by reinforcing the tourists' experience in certain places. Till recently Croatian geographic characteristics presented key elements of market differentiation. However, the importance of food for tourism development has been recognized by Croatian authors. They mostly point out food as the driving force for health tourism and gastronomic offer preconditions for the development of rural tourism. This article addresses two dynamic segments of the economy: agriculture along with food production and tourism. In thisarticle we try to find out whether food presents an effective instrument for enhancing rural tourism development in a country emerging from war and transitioning from central economic planning to a market economy. Using data from a study on the sample of 300 tourists in 12 tourist destinations in Croatia, the authors find that foreign tourists mostly buy food during their holidays in Croatia due to high quality of the Croatian local food. Also, a large number of respondents think that Croatian food is more expensive than the food in their countries and experienced difficulties in obtaining and consuming local food. The main problem is that some Croatian tourists' entities have failed to promote local food and regional cuisine which results in ignorance on the part of tourists, thus also contributing to lower demand and consumption levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".