Exclusion in #MeToo India: rethinking inclusivity and intersectionality in Indian digital feminist movements
Bibliographic record
Abstract
The #Metoo movement spread globally to include women in India who were employing social media platforms to discuss their experiences in sexual abuse and harassment. This paper investigates through literature review and data collection, why #MeTooIndia demonstrates a non-inclusivity towards marginalized, and gendered bodies and narratives on the Twitter platform. This exclusion is primarily the product of increased attention to issues of sexual abuse among the Indian elite including Bollywood celebrities, journalists, politicians, and well-known media personalities who employ Twitter as a space for “coming-out.” Secondly, non-inclusivity is evidenced through lack of discussion on the question of sexual abuse, and harassment in the daily lives of Dalit, trans women, women of lower caste and class, and other marginalized and gendered communities that have vastly different experiences of sexual abuse than the elite, urban woman. Finally, exclusion is exposed through the sparsity of personal narratives under the same hashtags owing to masculine toxicity as well as the creation of unsafe spaces for gendered minorities to recount their experiences. This research employs theory of intersectionality to ultimately rethink how to design and organize feminist movements online in order to create safer, more inclusive, and intersectional spaces for feminist activism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".