Structure and Influencing Factors of the Global Cooperation Network of E-Sports Teams
Bibliographic record
Abstract
With the progress of information technology and the transformation of the global economy, the digital economy is increasingly showing rapid growth and is becoming a key force in restructuring global factor resources, the global economic structure, and the global competitive landscape. E-sports, which is an emerging cultural industry and sport, has great significance in promoting cultural exchanges among countries and enhancing their respective national soft power. Presently, owing to its professionalization, internationalization, and ecologization, e-sports enables broader and multidimensional connections between game participants. However, e-sports cooperation networks based on virtual communities have not yet received widespread attention. Therefore, this study uses the information database of the participating teams of three international e-sports events, namely, the League Of Legends World Championship, The International DOTA2 Championships, and the CS: GO Major, to explore the structure of transnational e-sports team networks and their evolution from a theoretical perspective of virtual communities. This study uses the social network analysis and the gravitational model methods to reveal the multidimensional proximity and national attributes that influence the e-sports cooperative network patterns. The results show that first, the spatial evolution of the global e-sports cooperation network shows rapid expansion and low density, weak association, and strong dynamic network characteristics. The number of nodes increases rapidly while the network density shows a fluctuating decrease. This indicates that the development of Internet technology and the increasing popularity of e-sports have drawn increasingly more countries to participate in international e-sports activities, and the node connection of the e-sports cooperation network tends to be decentralized as a whole. Second, the global e-sports cooperation network has evolved into five associations representing geographical regions: the European associations with Denmark, Sweden, Finland, and Germany as the core, the Asia-Pacific associations with China and South Korea as the main partners, the Commonwealth of Independent States associations with Russia and Ukraine as the main partners, the Latin American associations with Peru and Argentina, as the main partners, and the Intercontinental associations with the United States and Canada as the main partners. Third, the spatial structure of the global e-sports cooperation network is influenced by the interrelationship between countries and their respective industrial bases. Social and organizational proximities drive the formation of e-sports cooperation networks, whereas geographical and cultural proximities do not significantly affect e-sports team cooperation. The interaction between geographical proximity and social proximity on the intensity of e-sports cooperation reflects a substitution effect; scientific research expenditure, e-sports revenue, and e-sports strength are the key elements affecting countries' importance in e-sports cooperation networks. Conversely, economic scale and general factors such as economic size and education level do not have significant effects on global e-sports team cooperation. This reflects the uniqueness of the e-sports industry in a digital economy. This study contributes to the research on the reconfiguration of industrial organization networks driven by the digital economy. Furthermore, this study provides a reference for making China's e-sports industry internationally competitive by improving its e-sports training system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".