A proof of concept participatory study on virtual sound immersion: Developing an inclusive prototype to improve the experience of planning leisure activities outside the home
Bibliographic record
Abstract
Individuals with atypical sensory and/or cognitive profiles have little access to out-of-home activities during their free time. A lack of information on activities and the characteristics of the outside environment often prevents them from investing in or obtaining new experiences. When they do participate, they often encounter difficulties and various obstacles that lead to failed attempts. This may lead to disengagement and a significant reduction in their social participation. The aim of this study was to co-construct a prototype based on the user’s needs and evaluate its feasibility and social validity from an exploratory perspective. A participatory research model using a mixed method based on an iterative design thinking methodology was used. The results show that describing the sensory attributes of environments increases people’s sense of familiarity and self-evaluation of accessibility. As a proof of concept, a virtual sound immersion prototype has been developed. It enables users to explore the sound environment and project themselves into new activities, including the emergence of the idea, the journey to get there and the performance of the activity itself. This study establishes the first step in the development of inclusive assistive technology and discusses issues related to the universal accessibility of the device.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".