Locating the Indian Gendered Subaltern on Digital Platforms: Digital Activism in #section377 and #metooindia
Bibliographic record
Abstract
This work examines the relationship between technology and activism in India, and the role that digital infrastructures play in the development of gendered digital protest. Through a combination of textual discourse and visual analysis, and critical digital humanities, feminist and queer frameworks, I study the digital queer movement around #Section377, and the feminist movement around #MeTooIndia on Twitter and Instagram in India. Through this research, I demonstrate how social media platforms such as Twitter and Instagram shape discourse surrounding digital activism, and how digital technologies both enable and disrupt subaltern voices, narratives and bodies in Indian cyberspaces. The comparative study of digital gender movements uncovers how digital platforms empower subaltern gendered voices, enable the construction of digital identities, and facilitate the formation of affective networks of empathy and subaltern counterpublics of resistance through the use of protest hashtags in Indian and Indian diasporic communities in Canada. Simultaneously, however, this study illustrates that digital technologies also hinder the amplification of marginalized voices, and create barriers in participation, representation, and inclusion online. Despite the construction of safe spaces and subaltern counterpublics on Twitter and Instagram, both digital queer and the feminist movements in India are exclusive, and lack individual representation and voluntary participation of women and LGBTQIA+ groups online. This research traces the histories of gendered exclusion that emerge through far-right nationalist, homophobic, and misogynist discourse, and work to actively decenter marginalized voices online in English and regional Indian languages such as Hindi that occur both in the form of textual and visual rhetoric. Ultimately, this research disrupts and troubles the traditional notions of technological determinism, particularly in the Global South, and focuses on questions of digital access, participation, and representation of vulnerable communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".