COVID-19 and Manufacturing Industries in India and Role of Human Resources Management
Bibliographic record
Abstract
COVID-19 had put a halt to many manufacturing industries across the world, including India, and that led to one of the worst job crises. Manufacturing industry is one of the most important sectors in India that drives the economy. India’s manufacturing workforce in majority belongs to the unorganized sector which has progressed the economy in pre-COVID-19 times. The employees in the unorganized sectors have suffered immensely during the COVID-19 crisis. The Human Resources Management (HRM) experienced unprecedented challenges in understanding the crisis, employee challenges, and coming up with timely solutions to address these issues. The focus of the paper is to review the opportunities and challenges in the HRM policies and practices in the manufacturing industries during COVID-19 and provide recommendations to apply these findings and to develop a future HRM strategy in unorganized manufacturing sectors in India. To our knowledge this is the first review that addresses the potential role and challenges of HRM in the unorganized manufacturing sector in India during COVID-19 pandemic.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".