Bibliographic record
Abstract
Many researchers in the field of international business have argued that cultural proximity can positively influence bilateral trade. These researchers have attempted to develop proxy measures for cultural proximity, such as language, ethnicity, religion, and trade of cultural goods. However, the conventional measures failed to capture the time-variant characteristics of cultural affinity or digitization in international trade. As an alternative approach, in this study, we focused on cultural affinity in social media like YouTube. Based on the recent popularity of Korean pop (K-pop) on YouTube, we hypothesized that online consumption of K-pop content creates an affinity for Korea as a country, resulting in higher Korean exports. We used panel data analysis for YouTube comments on K-pop music videos that were published from the second quarter of 2009 to the third quarter of 2012; these comments were segregated on the basis of the users’ home countries. We found that the YouTube comments of each country in the current and previous quarters are significant predictors of Korea’s total exports and exports of consumer goods such as processed food, clothes, and cosmetics to that particular country.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".