Bibliographic record
Abstract
Students in US colleges and universities have organised esports teams since at least the early 2000s. Important milestones in the development include the establishment of collegiate Starleague in 2009 with 24 schools in the United States and 2 in Canada. By the mid-2010s, collegiate varsity programs emerged. In 2014, for example, Robert Morris University in Chicago (now Roosevelt University) announced scholarships for its League of Legends team as part of its athletic program. Founded in 2016, the National Association of Collegiate Esports (NACE) emerged as a possible governing body. Despite its contested status, NACE reported over 170 member institutions with over 5,000 student-athletes in 2020. In this chapter, I will trace the organisational development of intercollegiate esports. The discussion of governance illustrates historical parallels between college football and college esports with regard to converging but also competing interests of various stakeholders (e.g. students, faculty, administrators, athletic departments, and governing bodies).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".