Immersive Storytelling and Emotion Promotion: VR 360° Video of Accessibility in Toronto
Bibliographic record
Abstract
VR 360° videos are an emerging technology and perspective-taking medium which offers the audience an immersive experience in virtual reality environments. This Major Research Project (MRP) aims to investigate the effectiveness of VR as a medium to communicate social issues and how it promotes prosocial behaviours and emotions to the public. This project's artifact is an immersive VR 360° video that allows a more neutral perception of disability and portrays physically disabled people and wheelchair users living in Toronto. In addition, this project describes the production process, conceptualization decisions, technical challenges, and lessons learned. Post-experience feedback provided by the audience showed that the video prototype accomplished its aim. Besides, the responses were positive, although some aspects could be improved, and further studies should be conducted in future work. Wheelchair Perspective VR Journey is a 9-minute VR 360° video that can be viewed by mobile devices and head-mounted displays (HMD). The video intends to help the audience better understand the social topic of accessibility in Toronto and potentially promote their emotions and sense of responsibility towards this social cause.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".