MOBILE-EYES \nFOR CHANGE \nImmersive Reality Technologies and the Design of Human Services: \n(A CASE STUDY IN AUTISM SPECTRUM DISORDER)
Bibliographic record
Abstract
Virtual Reality (VR) is a nascent technology platform that through the use of headsets immerses users into feeling as though they are transported to a new world (or space) with a sense of presence and the fidelity of real life. The VR industry is expected by many to disrupt how we consume and experience media as well as reimagine countless industries including filmmaking, gaming, entertainment, education and healthcare. \nThe rise of immersive reality technology platforms like VR can be juxtaposed with the rising state of crisis that exists in the Autism Services System (ASD) in Ontario for both youth and adults. Currently in Ontario there is a complex system of services where many individuals need various degrees of support and treatment with few and difficult to navigate supports available for some and not for all. This includes assistance for aging adults with ASD and other developmental disabilities. The core of human services systems such as Ontario’s ASD system are the lived experiences of families/caregivers who navigate multiple services/ supports and provide care for their diagnosed loved ones. This major research project will explore the potential for VR technology to play a role in capturing and sharing the lived experience of families\\caregivers to impact the design of services within human services systems in Ontario through the lens of the ASD system. The project will conclude by offering implications and directions for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".