Transformative Experiences Become More Accessible Through Virtual Reality
Bibliographic record
Abstract
Virtual Reality (VR) has immersive powers that can teleport an im-mersant into a virtual world and provide them with an experience of being somewhere that they may not have been able to go to. These powers of VR are most often used for games and entertainment, creating a space for escapism and isolation that may have negative psychological and societal outcomes. In this paper, we argue for an opposing application of VR technology - for promoting wellness and feeling of connectedness with people and the world around us. Such feelings can be elicited as a result of a profound awe-inspiring experience, that expands one's mental model and consequently leads to a positive behavioral change. Such experiences are described as transformative, or in strong cases 'pivotal'. Unfortunately, these experiences are rare, only accessible by some people, and nearly unavailable for researchers interested in studying this phenomenon. The immersive powers of VR present a unique opportunity to reproduce such experiences in the lab or at home, thus making them accessible both to the public and to the researchers. Having real-time access to an experience of the immersant will allow the researchers to study the progression of the tranformative experiences and understand its effects and precursors. In this paper, we are proposing a framework through which transformative experiences can be studied in VR. Understanding this phenomenon will inform how VR experiences should be designed in order to create a positive impact on our society.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".