A STUDY OF COVID-19 ON INDIAN ECONOMY
Bibliographic record
Abstract
Objective: The COVID-19 outbreak has wreaked havoc on India's economy. This article examines how India has dealt with all of the country's severe economic problems and how it has dealt with them through various government programmes. Methods: This research article based on secondary data. Different secondary sources, such as websites, government publications, journals, magazines, and newspaper articles, are preferred for acquiring information. As a result, the utilisation of a comprehensive Literature Review approach was used to make the current research signicant. Results:All economic activity was halted as a result of the shutdown, and individuals lost their employment. Manufacturing and service industries have completely shut down. In India, during COVID-19 pandemic worker migration had begun. The covid-19 epidemic had a signicant impact on every economic sector (tourist, retail, manufacturing, aviation, infrastructure, stock markets, and etc.). Due to the COVID-19 pandemic outbreak in the rst quarter of 2020-21, India's GDP growth rate fell to -24.38 percent. However, the government's scal policies and the Reserve Bank of India's monetary policies aid India's economic recovery. India attracts foreign direct investment because major corporations have lost faith in China, and all manufacturing activity has moved to another country. India has made a concerted effort to attract these countries. Conclusions: In this COVID-19 pandemic, India implemented a strict lockdown, which resulted in higher unemployment, lower GDP growth, and starving people migrating. India faced a critical scenario during COVID-19 due to a lack of health facilities. However, new concepts were explored during Covid -19 pandemic such as work from home, digital education, and a growth in social media marketing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".