Mobilizing Shakti: Hindu Goddesses and Campaigns Against Gender-Based Violence
Bibliographic record
Abstract
Hindu goddesses have been mobilized as powerful symbols by various groups of activists in both visual and verbal campaigns in India. Although these mobilizations have different motivations and goals, they have frequently emphasized the theological association between goddesses and women, connected through their common possession of Shakti (power). These campaigns commonly highlight the idea that both goddesses and Hindu women share in this power in order to inspire women to action in particular ways. While this association has largely been used as a campaign strategy by Hindu right-wing women’s organizations in India, it has also become a strategy employed in particular feminist campaigns as well. This article offers a discourse analysis of two online activist campaigns (Priya's Shakti and Abused Goddesses) which mobilize Hindu goddesses (and their power) in order to raise awareness about gender-based violence in India. I examine whether marginalized identities of women in India, in relation to caste, class and religious identity, are represented in the texts and images. To do so, I analyze how politically-charged, normative imaginings of Indian women are constructed (or maintained). This analysis raises questions about the usefulness of employing Hindu goddesses as feminist symbols, particularly in contemporary Indian society, in which communal and caste-based tensions are elevated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".