Study of the Negative & the Positive Impact of Coronavirus Pandemic on Different Types of Industry, Businesses & the Society
Bibliographic record
Abstract
Wesleyan Journal of Research, UGC Care-listed | ISSN: 0975-1386 | Peer-reviewed Journal Vol. 13 No. 69 (March 2021) Research Article: Management Study of the Negative & the Positive Impact of Coronavirus Pandemic on Different Types of Industry, Businesses & the Society Shubham Parsoya and Dr. Asif Perwej School of Management Studies (SOMS), Sangam University (SU), Bhilwara, Rajasthan, India Abstract: In the background of unforeseen occurrence scenario of COVID-19 nationwide pandemic, this paper has created an endeavour to study the impact of coronavirus pandemic (COVID-19) on differing kinds of companies and industries in numerous ways. Once the emergence of Corona pandemic in India introduced, the matter of public health condition has become core issue of the people in India and it's been on the forefront in conjunction with alternative and related serious issues of migrant labour, loss of employment, economic and industrial instability etc., and on the other side with the massive population size and its ever growing rate over the last 3 decades, the general public health scenario has been deteriorated significantly, and the occurrence of COVID-19 since 2019 has changed the entire world’s functioning accordingly. Internationally the Pandemic of COVID-19 has affected all sections of the economy and nobody will specifically predict when the pandemic is going to be over and everything are going to be like our past days. With the beginning of the unfold of COVID-19, business and various other industries, such as; region trade, transport industry, food industry, agriculture industry, housing industry, education industry, cinema industry, energy industry, producing industry, music industry, mining trade, were straightaway clean up, and the outside travelling and domestic travel activities were also withheld for the unknown length on such moment. The impact of Covid-19 on all such industries was very big. Globally all business and industry are witnessing serious threat with the spreading of COVID-19. All the countries have the first preference of the protection of the people, interference of unfold and health care of the infected folks. However, there are some types of industries that are becoming some extreme number of advantages from such COVID-19 pandemic conditions in some direct or indirect ways. Such industries are; Education (EdTech) industries, E-retail industries, Banking, financial services and insurance industries (BFSI). Medical sector, Information technology and knowledge Technology Enabled Services, Etc. Keywords: Pandemic, COVID-19, Industries, Businesses, Impact, Problems, World, Economy Conference Details:- International Conference on Analytical & Interdisciplinary Research – 2021 (ICAIR-2021) 05th - 06th February, 2021 https://www.sangamuniversity.ac.in/inner-pages/ICAIR-2021.php Description about the author: - Mr. Shubham Parsoya, Ex-Assistant Professor, School of Management Studies Ph.D. Doctoral Researcher (Business & Management) Master of Business Administration (MBA) in (Human Resources Management and Marketing Management) from Guru Gobind Singh Indraprastha University, New Delhi, India Bachelor of Commerce (B.Com.) Lean Six Sigma Champion Certified Professional (LSSCCP), School of Business Leadership Colorado, United States Six Sigma Yellow Belt Certified Professional (SSYBCP), Project Management Institute Accounting Fundamentals Certified Professional (AFCP), Corporate Finance Institute, Vancouver, Canada, United States
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".