The Economic Impact of COVID-19 in India and the Effect of Agricultural Farm Laws on Farm Sector Resilience
Bibliographic record
Abstract
COVID-19 was recognized as a pandemic in early 2020 which resulted in lockdowns, social distancing and border closures to both goods and people globally. The impacts to the agriculture sector and farmers included instability in markets and farm prices, disruption of supply chains, and impacts to farmers including migrant worker’s health and livelihood. The World Bank considers India to be a lower-middle-income country (LMIC) and disruption of agriculture has profound implications. In India a majority of the population is engaged in the agricultural sector and two-thirds of its household expenditures are for food. Each year the Government of India announces procurement (support) prices for the main agricultural commodities with purchase operations organized through public agencies. Prices, supply chains and farm labour are intricately linked to both income and consumption of farmed crops and vulnerable to disruptions from COVID-19 impacts. We review the situation in India regarding COVID-19’s impacts on farmers, migrant workers and the agricultural sector. And also review the government response and impacts of three introduced farm laws designed to reduce COVID-19 impacts to the agricultural sector. The inter-relationships between farmers, government procurement policy, and agricultural laws are explored. The new farm laws implemented with good intentions resulted in widespread farmer protests, lawsuits, mistrust of government, and greatly affected farmer resilience with unexpected results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".