Leveraging Food Culture in India to Promote Nutritious and Sustainable Food Preferences
Bibliographic record
Abstract
Food culture is often understood as the practices, beliefs, and traditions surrounding food and eating within a particular society or community.It encompasses various aspects of traditional dishes and recipes, culinary techniques and cooking methods, mealtime rituals, social aspects, and symbolism.Food culture in a critical factor shaping food choices by influencing eating and dietary norms and habitual behaviours.Given this, it is essential to clearly define the dimensions of food culture (particular to a country or region) if one is to seek to leverage its profound impact on individuals and communities.This working paper thus explores the multidimensional nature of food culture in India, emphasising its deep-rooted connections to cultural identity, social bonding, and wellbeing.The study, based on comprehensive reviews and stakeholder interviews, identifies six core principles shaping food culture:1. Selective Eating -Religious and caste influences on food choices. Sharing -Social norms dictating collective versus individual eating.3. Participation -Gender roles in food preparation and consumption.4. Openness -Globalisation-driven acceptance of diverse foods.5. Affluence -Economic disparities affecting food access.6. Wellness -Health narratives influencing nutrition choices.These principles are further shaped by globalisation, technology, and economic factors, impacting individual and societal food preferences.Considering India's current political and social landscape, the study prioritises wellness, participation, and openness and explores how a food culture programme can be designed using media engagement targeting India's youth. KEY MESSAGES• Food culture is often overlooked despite its key role in shaping food preferences.• It operates at both macro (societal norms) and micro (individual habits) levels.• Understanding its core principles reveals how it influences society and individuals.• In India, three key principles were identified: wellness, participation, and openness.• The paper focuses on participation, highlighting its beliefs (food preparers as guardians), values (duty to nourish), and norms (meals fostering relationships).• It explores how participation can be leveraged through youth and media to promote nutrition and sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".