MONETIZED COMPETITIVE PEER-TO-PEER SKILL-BASED GAME PLAY–AN INTRODUCTION
Bibliographic record
Abstract
The COVID pandemic has changed the world in ways, both large and small, forever. One unexpected way the world changed was when live sports suddenly went dark and esports began experiencing attention from betting audiences that were suddenly starving for content.1 Esports also received a second glance from gaming regulators. And, though not exactly suddenly (even though it felt like it), approvals for wagering on esports were granted in a variety of jurisdictions, most notably Colorado and Nevada.2\nA subset of the larger esports environment, peer-to-peer (P2P) skill-based play is hardly a new concept. You can find it in countless activities from basketball pick-up games at the park to arcade games and video games. Though monetized P2P skill-based game play is not novel either, the rise of game platforms allows causal and hyper-casual video game players the chance to wager on their individual performance.\nSkill-based game platforms enable immediate game play that can be either synchronous or asynchronous, allow for the matching of competitors of relatively equal skill, provide the rules for various competitions and tournaments, ensure that they are complied with, and offer a level playing field. One of the most desirable characteristics of P2P competition is the agnostic approach to outcome. Platform providers have no interest in who wins or loses, they simply provide the medium for the competition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".