Deployment of Virtual Reality (VR) to Promote Green Burial
Bibliographic record
Abstract
Population projections for Hong Kong suggest that the city will accommodate 8.22 million people in 2043. One in every three people are expected to be older than 65 in 2066. The long-held Chinese traditions for burial of deceased with reverence and honour, coupled with the chronic land shortage have presented an excessive demand for cemetery space. Niches are seldom recycled and the inadequate supply of new columbarium niche requires the family of the deceased to consider alternative way for keeping cremated ashes. To ease the demand, “green burial” has been launched and promoted by the HKSAR government through different print and social media. Currently, scattering of cremains in Gardens of Remembrance or at sea are the two common ways to perform green burial. The public acceptance of green burial is still a questionable and is under-researched. This study is going to deploy innovative technology, virtual reality (VR) to increase physical and psychological fidelity in highly resembled scenarios for the people. On one hand, VR gives immeasurable value to people when they are enabled to navigate different circumstances (physical fidelity) before considering the use of green burial. On the other hand, VR enables the people to engage in different mental processes (psychological fidelity) replicated from an array of cognitive reaction and sentiments with the choice of green burial. In order to optimize the configuration of the VR settings, we will conduct a face-to-face, semi-structured and in-depth interview with different practitioners. In the study, we explore: (1) To what extent the enhancement of physical fidelity of innovative technologies debunk public’s misconception of green burial? (2) To what extent the enhancement of psychological fidelity of innovative technologies debunk public’s misconception of green burial? (3) To what extent the simulated experience derived from innovation technologies change the public acceptance of green burial?
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".