Can Experiences With a Country's Foods Improve Images of That Country?
Bibliographic record
Abstract
ABSTRACT This study examines how experience with other countries' foods affects people's images of those countries, as well as the strength of such images. The findings show that foods are effective cultural elements that can enhance country images. People who have eaten a country's foods reveal more favorable country images than those who lack such experiences. Country images are even more favorable when people have more positive and many food experiences. Furthermore, positive country image effects are greater for people in opposite-hemisphere countries than for those in neighboring countries. Finally, people who prefer certain attributes of a country's foods provide more favorable ratings for related product attributes from that country, indicating affect transfer from food experiences to other product categories. These findings have key implications, especially for policy makers and researchers who seek effective ways to improve country images. KEYWORDS: Country-of-origin effectscountry of origincross-cultural marketingglobal consumer cultureKorea
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".