360-Degree Video for Whole Scene Capture: From Immersive Realism to Immersive Holism in Place-Based Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it