CHANGES IN THE LABOR MARKET IN QUARANTINE RESTRICTIONS: GENDER ASPECT
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».