360-degree video for virtual place-based research: A review and research agenda
Bibliographic record
Abstract
360-degree video is an immersive technology used in research across academic disciplines. This paper provides the first comprehensive review on the use of 360-degree video for virtual place-based research, highlighting its use in experimental, experiential, and environmental observation studies. Five key research domains for 360-degree video are described: tourism and cultural heritage; built environment and land use; natural environment; health and wellbeing; and transportation and safety. 360-degree video offers considerable advantages compared to unidirectional video, computer-generated virtual reality, and map-based geographic representation. Benefits include ease of use, low-cost, interactivity, sense of immersive realism, remote accessibility, and the ability to capture and analyze places in a fully panoramic field of view. Limitations include additional costs associated with virtual reality viewing technologies, simulation sickness and discomfort, and viewer distraction due to the technology's novelty and immersive affordances. This paper also outlines a future research agenda, including the possibility of moving beyond the ‘testing and trialling’ of 360-degree video since it provides novel research opportunities distinct from either ‘real’ experience or conventional forms of visual and spatial representation. Overall, this paper provides detailed evidence for researchers interested in using 360-degree video for virtual research on built, social, and natural environments and human-environment interactions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".