Interorganizational and International Networks For Digital Diplomacy Outreach: A Comparative Study of South Korea and China
Bibliographic record
Abstract
This study introduced an analytical framework for assessing the inter-organizational network system and the web impacts of public diplomacy organizations’ sites through the cases of South Korea and China. This study compared interorganizational collaboration networks, the impact of the government agencies’ websites, and the sectoral and geographic distribution of information resources offered by the agencies on the web. Social network analysis was employed, and it indicated that the Chinese public diplomacy organizations constructed denser and more strongly connected networks than the Korean public diplomacy agencies. Furthermore, the results suggested that .com was the most popular generic top-level domain, followed by .org, .net, and .edu, for both Korean and Chinese organizations. The source sites that sent links to Korean organizations originated mostly from East Asian (Korea, Indonesia, and Japan) and European countries (Germany and Russia). Information about Chinese culture was spread more widely across diverse countries, including East Asian (China and Japan), North American (Canada), European (United Kingdom, France), and Oceanian (Australia) countries. For both Korea and China, domestic audiences played key roles as information hubs in each network, which illuminates a networked and cooperative form of digital diplomacy outreach in these countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".