Definitions of Esports: A Systematic Review and Thematic Analysis
Bibliographic record
Abstract
The esports market has been growing exponentially has been growing exponentially with much interest from industry and academia. Perhaps because of this growth, there is a lack of agreement on what esports actually encompasses. We conducted a systematic review of 461 peer reviewed, full papers that provide a definition of esports. Findings highlighted the growth of the esports field across different domains, and increasing global interest in esports, but a lack of consensus regarding definition of the term. Through thematic analysis we identified nine dimensions across esports definitions. We critically assess these dimensions in terms of their representativeness and utility in describing the multifaceted nature of esports. Our work may help create a shared understanding of what esports is- and is not-capturing a diversity of experiences within organized competitive gaming and supporting continued research growth in this increasingly important domain.
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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 | 0.069 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.047 | 0.039 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".