Intersections between Technology, Journalism and Civic Participation in India
Bibliographic record
Abstract
The adaptation of evolving technology in Indian filmmaking has been a process of embracing it as a valuable tool rather than perceiving it as an alien imposition from formerly colonized nations. Developments in telecommunications since 1995 have profoundly impacted filmmaking in India, from the technology employed to the evolution of non-fiction storytelling. In an environment where mainstream journalism faces challenges and grassroots media gradually gains viewership on digital platforms, immersive journalism can enhance civic participation by providing a heightened sense of 'immersion' and 'presence' compared to traditional two-dimensional formats. This paper examines 360-degree immersive journalism videos produced by ElseVR, a non-fiction subsidiary of Mumbai-based Memesys Cultural Lab, a pioneer in mixed-reality filmmaking in India. During the first wave of VR immersive non-fiction, ElseVR released app-based quarterly magazines featuring 360 video documentaries. These films not only offered narrative experiences but also encouraged viewers to assume various perspectives while watching them. Utilizing Nash's (2022) concepts for interpreting first-person experiences in VR documentaries, this study employs the positions of tourist, encounter, and witness to analyze three immersive journalistic documentaries using ElseVR's technology: Nishtha Jain's Submerged (2016), Naomi Shah and Pourush Turel's Caste is Not a Rumour (2017), and Faiza Khan's When Land Is Lost, Do We Eat Coal (2016). Each position provides insight into the experience of entering unfamiliar spaces, as interest, curiosity, and the VR environment’s affordances give rise to a multi-sensory experience where the positions of tourist, encounter, and witness uniquely intersect. In India, where digital and smartphone penetration varies significantly, the potential for widespread adoption of such technology remains uncertain. However, the intersections between journalism, civic engagement, and technology in ElseVR's documentaries are noteworthy. By avoiding a technologically deterministic perspective, a heightened understanding of VR technology's role in journalism from non-Western environments could enhance civic participation and encourage reevaluating emerging media practices in the Global South.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".