Dharma Nudges: A Culturally Grounded Behavioral Intervention Framework for Misinformation Prevention in Indian Digital Ecosystems
Bibliographic record
Abstract
Misinformation on WhatsApp and other Indian digital platforms has triggered communal violence, lynchings, health crises, and democratic erosion. Western accuracy nudges, effective in individualistic WEIRD populations, show limited or inconsistent impact in India due to cultural mismatch. This paper introduces "Dharma Nudges," a culturally grounded behavioral intervention framework that translates indigenous ethical concepts (dharma, satya, sidq, sach, loka-sangraha, guru-bhakti, pitṛ-dharma, and "log kya kahenge") into short, relatable prompts delivered at the moment of forwarding. Instead of asking "Is this accurate?", these nudges activate moral identity, anticipated social emotions (honor, shame, pride), and relational accountability through questions such as "सत्य बोलना हमारा धर्म है। क्या आपने जाँच की?" or "अमानत में खयानत नहीं करते। क्या आपने तस्दीक की?" Grounded in moral-identity theory, dual-process models, and collectivist social psychology, Dharma Nudges outperform universal accuracy reminders in Indian family and community chats by aligning with relational truth concepts and high-context communication norms. The framework offers pluralistic, interfaith, and constitutional variants for Hindu, Muslim, Sikh, Christian, Gandhian, and secular audiences. Initial mechanistic lab studies (2023-2025) and qualitative fieldwork support stronger activation of reflective pausing and identity-congruent restraint compared to standard nudges. Large-scale randomized field trials on WhatsApp are in preparation. Keywords: Dharma Nudges, misinformation India, cultural nudges, behavioral interventions, WhatsApp misinformation, fake news prevention, relational accountability, moral identity, collectivist psychology, accuracy nudges India, sidq, satya, loka-sangraha, scientific temper nudge, Indian digital ecosystem, family WhatsApp groups, cross-cultural behavioral science, 2025
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".