Gender and Social Institutions in the Labour Markets: An Analytical Perspective on the Covid-19 Disruptions in Northeast India
Bibliographic record
Abstract
This article examines the trends and patterns of unpaid work performed by women in India’s North Eastern States and account for the factors that underlie these trends. It uses the two unit-level datasets from the National Sample Survey Office Employment and Unemployment Survey 2011–2012 and Periodic Labour Force Survey 2018–2019. The multinomial regression results found that illiterate and lower social stratum have more chances to engage in unpaid activities. It then explores the impact of COVID-19 on unpaid work activities among women in the northeast states. The telephonic conversation and informal interviews with different regional stakeholders have been substantiated along with the utilisation of the Centre for Monitoring Indian Economy report on employment and unemployment for the second quarter of 2020 for nuanced analysis. The study found that women are losing their livelihood very fast during the pandemic and the effects are likely to linger for a more extended period. JEL Codes: J16, J21, J22, R23
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".