Mersivity: XRSC (eXtended Reality Spatial Computing) for Health, Well-Being, and Accessibility (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED This paper provides an overview of Mersivity, also known as Vironmentalism or XRSC (eXtended Reality Spatial Computing) with applications in health, well-being, and accessibility. There is a growing interest in technologies that are ’mersive (immersive, submersive, or supermersive), i.e. technologies that encapsulate or enclose or surround us. Examples include “wearables”, immersive VR (Virtual Reality), and electric vehicles such as self-driving cars, vessels, and personal aircraft (ABC = Aircraft, Boats, Cars). It is widely agreed that there is a risk or danger of asymmetry of immersive technologies that can separate us, harmfully, from our surroundings. In order to support human health, well-being, and accessibility, it is essential that we can ’merse any of these technologies that ’merse us. If one can’t go for a hike in the forest or along the beach (and maybe go for a swim as well) with the technologies that claim to help us, then those technologies are actually harming us. Thus an important element of Mersivity is a connection to the physical world = the world of “atoms” = nature = the environment = sustainability = our surroundings. Mersivity therefore is at the nexus of the physical (nature/sustainability/environment), virtual, and social worlds. In this paper we provide a historical perspective on XR+SC, from the early spatial computers of the 1970s, the introduction of XR=eXtended Reality in 1991, and leading up to the latest trends such as the “Freehicle” = vehicle of freedom for those with disabilities. See Fig 1.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.040 |
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".