CHANGES IN THE LABOR MARKET IN QUARANTINE RESTRICTIONS: GENDER ASPECT
Bibliographic record
Abstract
The economic crisis caused by the current pandemic that has led to a sharp decline in demand in both the Ukrainian labor market and globally. Employment in the world in 2020 fell not only due to job loss, but also due to inaction: people left the labor market because they could not work due to lockdowns. Problems in the labor market affected more women than men. In all regions of the world, women are more likely to become economically inactive, in other words to drop out of the workforce during this crisis. One of the groups of women at particular risk are women in various occupations who have children of preschool or primary school age. As a result, more than a quarter of working women are considering slowing down their careers or giving up work altogether due to the forced stay of children at home. The most popular women's professions are related to education, training, care and intensive interpersonal communication. The most risky areas in terms of virus infection - medicine, education, household services, retail trade, etc. - are represented mainly by employed women. Considering the specifics of pandemic measures, it is obviously that the service sector has suffered the most from lockdowns and quarantine restrictions. Similar data are published in European and American statistical reports. More women than men are employed in personal care, cleaning and education. The solution of this problem can be considered in three ways simultaneously: at the level of households, enterprises and the state. For households, a more proportionate redistribution of a woman's responsibilities to other family members may be a possible outcome. Businesses may also take a number of measures to mitigate the impact and offset gender disparities in the labor market caused by quarantine restrictions. The state should consider and approve short-term support scenarios for families with children, examples of which can be seen in European countries. Public policy in the long-term should be aimed at forming and introducing into society the norm of the need for active engagement men in household chores and child care on par with women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".