Bibliographic record
Abstract
For the last two years, the global pandemic of Covid 19 caused by SARS CoV-2 has emerged as an immense burden on the health and economic system worldwide affecting several countries. Over 30 million people have been infected by the corona virus in India. Covid 19 can infect people of all genders and Ages. More than 45 percentages of Indian households lost their income due to the Covid 19 Pandemic then the previous year. The Indian Economy was expected to loss around Rs. 32,000 crores in the initial stage of pandemic itself. Almost all sectors of the economy has been adversely affected. Exports are sharply declined. Food and Agriculture, Aviation and Tourism are worse affected. Telecommunication and Pharmaceutical companies are having comprehensive advantages and continuously developed in the pandemic situation. Global level, the impact of Covid 19 is negative and creates high level of inequality, pain and strain, gender equality and gender strategy management are adversely affected. The study based on secondary information collected from the Centre for Monitoring of Indian Economy from the various quarter and World Bank Report during the pandemic period. The paper focused on unemployment rate in different sectors of the Indian economy and the violence against for Women in India during the covid 19 periods. As per International Labour Organization (2021), about 400 million workers in India are at the risk of being pushed into poverty due to cause by lockdown and Covid 19. The unemployment rate in India was recorded at 8 percent. Hence, detailed discussions are essential through this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".