COVID-19'S Impact on Underplanning: A Bibliometric Analysis
Bibliographic record
Abstract
Context: The coronavirus has recently acknowledged its own illness. Corona is spreading quickly via at least 180 countries globally. Starting in December, a large number of cases spread throughout Wuhan, China. It affects a great deal of people all across the world in terms of housing, transportation, food, and work prospects. Numerous people lose their jobs, experience unemployment, or become unemployed globally, which decreases the economic rate across the globe. The Indian economy lost more than 9% of its gross value added in that month as a result. There was a total lockdown for the first 21 days of the corona virus epidemic, which is believed to have cost the Indian economy more than 32,000 crore every day. It has an impact on businesses who manufacture products for the hotel sector as well. India’s jobless rate drops by 23% as a result of the closure. We infer from the data that the week after India’s first corona virus detection, the jobless rate dropped. A Mumbai-based think tank’s analysis indicates that India’s unemployment rate was 8.7% in March 2016. 43 months after September 2016 saw a high unemployment rate. This rate rose to 7.16 percent in January 2020. Farmer’s fields decreased by 14.53 percent outside of metropolitan areas and 13.08 percent inside them due to lockdown. The present crisis is distinct since it has increased in terms of length and unemployment rate. The two variables have never increased so quickly in such a short amount of time. Methodology: A precise phrase and a few selected phrases were used in a search query to exclude Central India from publications in the Web of Science Database. R-Studio Application was imported and looked through. Results: 68 papers from 180 sources were included in the main list of publications. Most of them were journal entries. There were more corresponding authors from India in addition to a few more authors from 51 other nations, such as Germany, France, Canada, India, and the United States. The Index for teamwork is 4.82. For publishing, the annual percentage growth rate was 0.959. The Indian Society of Labor Economics (ISLE) will no longer perform employment research, which is the most severe immediate result of the COVID-19 problem, according to a study. Other long-term effects include slower economic development and increased inequality. 520 ISLE participants completed the online survey, which was launched in the last week of May.
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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 | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.086 | 0.179 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".