Extension or Diversification? A Network Analysis of Sponsor Integration for the 2023 Electronic Art Sports FIFA Global Series
Bibliographic record
Abstract
The creation of esports competitions by traditional sports properties has been a significant contributor to the growth and commercial success of the esports industry. Major leagues, clubs, and athletes alike have sought to capitalize on the growth of esports competitions by launching their own competitions and supported competitors. Component to the success of these traditional sporting entrants into the esports space has been the verisimilitude of esports games to their sporting counterparts. This research examines a unique context of this gaming realism through the context of sponsorship programming, exploring the extent to which sponsors of traditional football leagues extend their partnerships to that organization’s esports competition. Taking a network analysis approach, the study collated all sponsors of 22 participating leagues and federations in EA Sports’ FIFA1 23 Global Series and conducted a two-stage analysis of the network created. The study’s findings suggest that little integration exists between league sponsors and their esports counterparts, with minimal centrality and interaction present in the sponsor network. These findings advance our understanding of esports sponsorship and provide new insight into the sponsorship applications of networks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".