Bibliographic record
Abstract
ElseVR, the non-fiction offshoot of Memesys Cultural Lab, based in Mumbai India is one of the pioneering filmmaking labs working with mixed reality in India. It has been releasing 360 video documentaries as a VR app based quarterly magazine that demands spectatorship as well as engagement by way of the subjects it chooses to talk about. In a country where severe inequities of access are present, with digital and smartphone penetration being reasonably fragmented across the country, the feasibility and acceptance of such technology on a larger level remains to be seen. What is interesting however, are the intersections between civic engagement and technology in ElseVR’s documentaries. The documentaries are not only narrative in nature, but also employ and encourage the spectator to imagine different positions while viewing them. Using the concepts proposed by Nash (2022), in making sense of first-person experience in VR documentary, the researcher employs the position of a tourist, encounter, and witness (2022:108) while viewing three documentaries using ElseVR’s technology as a medium. The three films are Nishtha Jain’s Submerged (2016); Faiza Khan’s When Land Is Lost, Do We Eat Coal (2016) and Naomi Shah and Pourush Turel’s Caste is Not a Rumour (2017). Each position leads to an understanding of the experience of what it is like to enter a space that is not one’s own. Along with the researcher’s own interpretation, and an analysis of the supporting material on the documentaries (such as publications by humanitarian organizations on the topic of the documentaries), news media reports highlighting the issues, and online community conversation received from viewers on Else VR’s Facebook and Youtube channels was conducted to make sense of the overall engagement with the documentaries besides a first-person experience. Considerations of interest and inquisitiveness towards the content, and the affordances offered by the VR environment gave way to a multi-sensory experience where the positions of tourist, encounter and witness overlapped with no conscious intent. Going back and forth with the virtual community conversation around the films and the researcher’s experience of immersion with the documentaries, it was a heightened awareness of VR technology’s role in documentary filmmaking in non-Western environments. Avoiding a technologically determinist gaze, it was the larger purpose of VR journalism that stood out to bring to light stories that deserve more civic engagement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".