A Best-Fit Framework and Systematic Review of Asymmetric Gameplay in Multiplayer Virtual Reality Games
Bibliographic record
Abstract
Increasingly, virtual reality (VR) design and research leverages gameplay asymmetries, flattening discrepancies of interface, abilities, information or other aspects between players. A common goal is to induce social interactions that draw players without head-mounted displays into a shared game world. Exploring these asymmetries resulted in many artifacts, creating an innovative yet disparate research landscape that showcases points for improvement in coverage of the field and theoretical underpinnings. In this article, we present a literature review of asymmetry in multiplayer VR games, using a framework synthesis method to assess the field through a lens of existing literature on asymmetries in gameplay. We provide an overview of this emerging subfield and identify gaps and opportunities for future research. Moreover, we discuss how research artifacts address prior theoretical work and present a “best fit” framework of asymmetric multiplayer VR games for the community to build upon.
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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 | 0.027 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.037 | 0.025 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".