eSport: Construct specifications and implications for sport management
Bibliographic record
Abstract
The purpose of this article is to add to the conceptual discussion on eSport, analyze the role of eSport within sport management, and suggest avenues for future eSport research. The authors suggest that debates surround the degree to which eSport represents formal sport, and disagreements likely stem from conceptualizations of sport and context. Irrespective of one’s notion of eSport as formal sport, the authors suggest the topic has a place in sport management scholarship and discourse. Such a position is consistent with the broad view of sport adopted by Sport Management Review, the perspective that eSport represents a form of sportification, and the association among eSport and various outcomes, including physical and psychological health, social well-being, sport consumption outcomes, and diversity and inclusion. Finally, the authors conclude that eSport scholarship can advance through the study of its governance, marketing, and management as well as by theorizing about eSport.
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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 | 0.094 | 0.185 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".