Korea's Response to COVID-19 According to Set Time Frames, With a Focus on the Network Between the Government and Responding Agencies: Social Network Analysis
Bibliographic record
Abstract
BACKGROUND: In December 2019, COVID-19 was first confirmed in Wuhan, China, and as the respiratory disease spread around the globe, there was a spike in interest worldwide in combating such contagious diseases. When such disasters occur, the central government of South Korea and its affiliated local governments-together with nongovernmental organizations-play a crucial role in crisis management systems. OBJECTIVE: The purpose of this paper is to corroborate the characteristics government ministries and domestic and foreign institutions exhibit through their interconnection when the parties are undergoing a disease-related catastrophe such as the COVID-19 pandemic. METHODS: Using the social network analysis technique, the span of the COVID-19 pandemic was segmented into 3 time frames, and the relational characteristics of the COVID-19 contagious disease response department and related agencies at home and abroad were analyzed based on 3 centralities. RESULTS: Evidence from the second and third time frames indicates that the agents reacting to contagious diseases do not necessarily hold the central position in the network. From this, it can be inferred that it is not only the primary host that plays a pivotal role but the key to a successful response to various disasters also lies in cooperation with the relevant parties. CONCLUSIONS: The incongruency between the findings of this paper and the existing disaster response system gives rise to the corollary that both the essential parties and the adjoining ones need to collaborate for a coordinated crisis response in disaster situations. Furthermore, much significance lies in the fact that this paper explores the various aspects that could surface among the host and relevant parties in a real-life pandemic.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".