Bibliographic record
Abstract
Over the last two decades, India's national capital city, New Delhi, has witnessed an intense physical-socio-cultural metamorphosis with several concurrent developments - the expanding operations of the Delhi Metro Rail network, the increasing access to and decreasing costs of digital media technologies, and the rise of Internet-based social media. Interconnected smart mobile devices act as sites for community engagement through social media, such as Instagram - a social networking site with the highest number of users from India. The inauguration of new modes of visuality and sociality through the mobility of Instagram draws attention to the city's residents' mediated lives and everyday encounters in liminal spaces such as the Delhi Metro. In earlier work on the Delhi Metro and Instagram, I have engaged with the notion of 'place' and the complexities of relationships between individuals, communities, images, digital technologies, urban spaces, and the transformations in their everyday practices. The point of departure for this paper is re-reading the idea of "imagined communities". The current work explores the socially constructed Delhi Metro commuters' communities as represented on the Instagram account @metrodoodle by a Delhi Metro commuter, Samar Khan, who is also an artist. How does he present his perceptions of these imagined communities' everyday mobilities, sensorial subjectivities, volatile socialities, and the mobile-mediated visualities of the changing city in the past decade? How may one read the ludic aesthetics of his creative artworks that represent a reimagination of a mosaic of narratives shared by everyday urban travellers? How do they capture tangible and intangible experiences of metro travel, and what kind of online conversations do they initiate? The paper examines how subjective imaginations on Instagram may articulate interesting observations about charged affect in private-public experiences in public spaces, commuters' shared values and quotidian experiences, and how the MetroDoodle account may serve as an immaterial community archive of their evolving sensory adaptations and mental experiences of the city. The paper employs a qualitative approach informed by digital ethnography, in-depth interviews, and visual and textual analysis of the artworks posted on the MetroDoodle Instagram account between October 2016 and September 2023. The work suggests how select commuters/users are experiencing the metropolitan city in online and offline modes, claiming and reimagining it and fostering diverse representations. New meanings are articulated, leading to an enhanced 'sense of place' and a 'sense of community'.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".