Understanding food insecurity in rural India: A comparative examination of farming and non-farming families during the coronavirus lockdown
Bibliographic record
Abstract
Food insecurity has become an increasingly pressing issue during the COVID-19 lockdown in India, particularly in the rural areas. The COVID-19 pandemic had a strong impact on livelihoods and economic activities, resulting in a significant increase in the prevalence of food insecurity. The objective of this study is to examine the sociodemographic determinants of food insecurity among the rural poor in India during the COVID-19 lockdown. Data for this study were extracted from the COVID-19-Related Shocks in Rural India 2020 survey (Round 2, 2020). Study areas included: Rajasthan (n=930), Uttar Pradesh (n=778), Bihar (n=1,073), Jharkhand(n=890), Madhya Pradesh (n=823) and Andhra Pradesh (n=511). Overall, 26.1% of respondents reported having reduced portion sizes or meals, 5.7% reported someone in their household going hungry, and 5.9% indicated they had run out of food. Participants from Scheduled Castes had a higher risk of reduced portion sizes (RR=1.54; 95% CI=1.26,1.89) and running out of food (RR=1.57; 95% CI=1.01,2.45). Male sex was associated with a lower risk of reduced portion sizes (RR=0.82; 95% CI=0.72,0.93). Compared to participants from Rajasthan, those from states such as Uttar Pradesh, Bihar, and Madhya Pradesh had significantly higher risks of experiencing all three food insecurity indicators. For instance, participants from Bihar had the highest risk for reduced portion sizes (RR=3.14, 95% CI=1.95,5.06), someone being hungry and not eating (RR=3.14, 95% CI=1.95,5.06), and running out of food (RR=2.79, 95% CI=1.66,4.68). The results indicate that when designing food security programs in rural India, it is important to consider factors such as gender, household size, religion, occupation, and region. Additionally, non-farming households faced higher relative risks of food insecurity, emphasizing the necessity for targeted interventions and support to address the vulnerabilities of these households.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".