“Four Asians and One American”—Import Player Discourse in North American Professional League of Legends
Bibliographic record
Abstract
The esport industry often seeks to depict itself as politically neutral, distinct from the discourses regarding race or nationality as found in traditional sports. However, Western esports in particular continue to reinforce racial dynamics that often center on Whiteness and reinforce the exclusion of people of color. The discourse surrounding migrant “import players,” within official broadcasts and online communities, poses important questions about globalized labor mobility and nationalism, paralleled in traditional sports. This project investigates broadcast and community narratives surrounding “import players” in the North American League of Legends ecosystem (also known as the League of Legends Championship Series) by examining Team Liquid’s roster through 2023 and 2024. Using the discourse of language and work ethic, the presence of Korean “imports” in the League of Legends Championship Series is emblematic of already existing techno-Orientalist anxieties of skilled, yet dehumanized Asian labor displacing the rightful positions of Western “native talent.” As seen by the addition of American player APA, Whiteness is positioned as a vital competent to success in Western League of Legends. Meanwhile, the identities of Asian diasporic players as “native talent” remain precarious and dependent on in-game success.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".