‘Welcome to selfiestan’: identity and the networked gaze in Indian mobile media
Bibliographic record
Abstract
This article examines selfie culture and visual social media in India by exploring how smartphone marketing and the performativity of the user–device interface intersect with the cultural influence of the Hindu ritual of darshan and the recent political success of populist Hindu nationalism. Darshan, a long-standing, everyday Hindu practice entailing an active visual and physical exchange between worshiper and devotional image or object, bears striking overlaps with the mechanics and conditions of the networked visuality of the self. Taking a critical technology approach, this article places scholarship on selfies and their production methods in conversation with anthropological descriptions of darshan and existing theories of darshan’s impact on media in South Asia. Theoretical exploration of concepts and practices is augmented by content analysis of social media imagery related to darshan. In arguing that aspects of traditional visual regimes may endure in personal networked media use in India, this work underscores the need for balancing globalized affordances and applications, on one hand, with culturally specific meanings and ideological frames, on the other, particularly as these converge in the visual performance of networked identity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".