1st International Workshop on Standardization in Cybersickness Research: “Establishing Standards for Cybersickness Measurement and Mitigation: A Community-Driven Approach”
Bibliographic record
Abstract
The pervasive challenge of cybersickness significantly hinders the widespread adoption and seamless user experience of virtual reality (VR) technology. The absence of standardized methodologies for assessing and mitigating cybersickness further complicates research efforts and the development of effective countermeasures. The 1st International Workshop on Standardization in Cybersickness Research aims to address this critical need by fostering a community-driven approach to establish benchmarks and best practices. The workshop will facilitate knowledge exchange and collaboration among researchers, developers, and stakeholders to collectively address the multifaceted challenges of cybersickness. Through interactive breakout sessions and discussions, the workshop seeks to achieve consensus on standardized metrics and methodologies for measuring cybersickness, evaluate and refine mitigation techniques, and explore the impact of individual differences and emerging technologies. The anticipated outcomes include the development of a standardized assessment framework, identification of best practices for mitigation, and a roadmap for future research, ultimately contributing to the advancement of VR well-being and user experience.
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.
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 | 0.065 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".