Data Feminism 101 - South Asian Context Handwritten Notes Template
What is Data Feminism in South Asia?
A framework for understanding how data systems in South Asia reflect and reinforce intersectional inequalities based on gender, caste, class, religion, region, and other identity markers.
Core Insight: In South Asia, data is shaped by colonial legacies, patriarchal structures, caste hierarchies, and development paradigms that often exclude or misrepresent marginalized communities.
Why Data Feminism Matters in South Asia
Invisible Women: Much of women's work (agricultural labor, care work, home-based production) remains uncounted
Caste & Data: Data collection often fails to capture caste-based discrimination and exclusion
Digital Divide: Technology solutions often exclude rural women, dalits, and minorities
Colonial Data Legacy: Classification systems inherited from colonial era still shape official statistics
The Seven Principles in South Asian Context
1. EXAMINE POWER
Definition: Analyze how power operates through data systems in South Asia
Power Structures in South Asian Data:
Caste hierarchy: How data categories reinforce or challenge caste divisions
Gender patriarchy: Male-dominated data collection and interpretation
Urban bias: Data systems designed for urban, formal economy
Language dominance: English/Hindi bias in digital platforms
Class exclusion: Smartphones and internet access requirements
Example: Aadhaar enrollment initially excluded many women who couldn't provide male guardian consent, or had no permanent address
Questions to Ask:
Who is collecting this data and from which communities?
What languages and literacy levels are assumed?
How do caste, class, and gender intersect in this dataset?
Notes Space:
2. CHALLENGE POWER
Definition: Actively work to dismantle data systems that perpetuate inequality
Challenging Data Power in South Asia:
Question official categories: Challenge binary gender classifications, inadequate caste data
Center marginalized voices: Prioritize data from dalits, adivasis, religious minorities
Resist data colonialism: Question foreign tech companies extracting data from South Asia
Demand transparency: Make government algorithms and AI systems accountable
Example: Activist groups challenging facial recognition systems that have higher error rates for darker skin tones
Notes Space:
3. ELEVATE EMOTION & EMBODIMENT
Definition: Value lived experiences and local knowledge systems
Embodied Knowledge in South Asia:
Traditional knowledge: Agricultural, medicinal, and environmental knowledge of communities
Migration experiences: Stories of displacement, seasonal migration, urban struggle
Violence and trauma: Communal violence, domestic violence, caste-based violence
Care work experiences: Emotional labor in families and communities
Example: Participatory mapping by slum communities showing their own understanding of neighborhood assets and risks
Notes Space:
4. RETHINK BINARIES & HIERARCHIES
Definition: Challenge classification systems that reinforce South Asian social hierarchies
Problematic Binaries & Classifications:
Rural/Urban: Ignores peri-urban realities and circular migration
Formal/Informal: Most work in South Asia is informal or mixed
Male/Female: Excludes hijra, transgender, and non-binary identities
Literate/Illiterate: Ignores multilingual competencies and oral traditions
Hindu/Muslim/Other: Oversimplifies religious identity and practice
Example: India's 2011 census adding "Other" category for gender, recognizing transgender people
Notes Space:
5. EMBRACE PLURALISM
Definition: Include multiple knowledge systems and data sources
Plural Knowledge Systems in South Asia:
Indigenous data: Adivasi community knowledge of forests, agriculture, weather
Religious traditions: Temple records, Islamic waqf data, Buddhist monastery systems
Community organizations: Self-help group records, cooperative society data
