On conducting ethically sound psychological science in the metaverse.
Bibliographic record
Abstract
As the next generation of the internet, the metaverse is an immersive three-dimensional (3D) world that incorporates both physical and virtual environments. The metaverse affords numerous advantages for advancing our theoretical and practical understanding of human cognition, emotion, and behavior, as well as shaping our methodological approach to conducting psychological science. However, undertaking research in a world that merges the physical and virtual, also presents new and unique ethical challenges that are not addressed by current ethical guidelines such as the Belmont Report, the Ethical Principles of Psychologists and Code of Conduct, and the Association of Internet Researchers Internet Research Ethical Guidelines. We discuss the different domains of the metaverse relevant to psychological research and consider how three categories of ethical challenges (i.e., "respect for persons," "beneficence," and "justice") may arise when conducting research in the metaverse. We also provide recommendations for addressing these challenges that include reconfiguring existing ethical guidelines as well as creating new ones. Together, these can inform and assist researchers and institutional review boards in making decisions about conducting ethically sound psychological science in the metaverse. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.469 | 0.550 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.022 | 0.035 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".