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Record W4307716635 · doi:10.1145/3549490

Definitions of Esports: A Systematic Review and Thematic Analysis

2022· review· en· W4307716635 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2022
Typereview
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRepresentativeness heuristicThematic analysisDiversity (politics)Thematic mapField (mathematics)Data scienceComputer scienceSociologyPsychologyGeographySocial scienceQualitative researchMathematicsSocial psychologyCartography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.328
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.171
GPT teacher head0.402
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it