360-Degree Video for Whole Scene Capture: From Immersive Realism to Immersive Holism in Place-Based Research
Bibliographic record
Abstract
360-degree video is an affordable and easy-to-use technology for social science research. It holds significant potential for capturing spatio-temporal aspects of the social world from a fully omni-directional spatial perspective; however, gaps remain as to how it can be used to support field-based data collection and analysis. In this short piece we offer two contributions to the literature on 360-degree video for qualitative social science research on place. First, we draw on evidence from our multi-city study of ‘urban platform temporalities’ to develop a step-by-step procedure for producing and analyzing 360-degree digital video datasets, demonstrating the potential of the technology for what we term whole scene capture . We provide practical advice on software, hardware, camera usage, video processing, and ethical considerations; and introduce the 360-video qualitative coding technique of spherical simultaneous perspective . Adding new evidence of its use to already established literatures on 360-degree immersive video ethnographies and virtual human-environment exposure research, our method for systematic 360-degree capture of spatio-temporal data is applicable to a range of social science studies with a field-based data collection component. Finally, drawing together technological understandings of immersion from the field of VR with its ethnographic meaning, we then articulate the notion of immersive holism as a quality of 360-degree video that enables deep, meaningful, and comprehensive knowledge of place.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".