Impact of COVID-19 on Indian Economy - Handling the Second Wave of Virus
Bibliographic record
Abstract
COVID-19 has proved to be worse than the Great Depression of 1930. The economy of all the nations collapsed due to the spread of the virus. The objective of this paper is to assess the impact of COVID-19 on Indian economy in the short term and long term.The fourth quarter GDP figures during the financial year 2020 went down to 3.1% according to the figures released by the Ministry of Statistics. On 15th March, the unemployment rate was 6.7% which rose to 26% on 19th April, 2020. During the lockdown, 140 million people lost their jobs. The primary sector (agriculture) has been severely affected by this pandemic. Major issues are related to labour availability, inability to access the markets for produce due to hurdles in transportation and market operations. This has also affected the sales of dairy products like fish, poultry, etc. Through this paper, we aim to analyse the current economic situation and policy measures that can be taken to tackle the problems. <br>
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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 | 0.000 | 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) | 0.069 | 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".