COVID-19'S Impact on Underplanning: A Bibliometric Analysis
Notice bibliographique
Résumé
Context: The coronavirus has recently acknowledged its own illness. Corona is spreading quickly via at least 180 countries globally. Starting in December, a large number of cases spread throughout Wuhan, China. It affects a great deal of people all across the world in terms of housing, transportation, food, and work prospects. Numerous people lose their jobs, experience unemployment, or become unemployed globally, which decreases the economic rate across the globe. The Indian economy lost more than 9% of its gross value added in that month as a result. There was a total lockdown for the first 21 days of the corona virus epidemic, which is believed to have cost the Indian economy more than 32,000 crore every day. It has an impact on businesses who manufacture products for the hotel sector as well. India’s jobless rate drops by 23% as a result of the closure. We infer from the data that the week after India’s first corona virus detection, the jobless rate dropped. A Mumbai-based think tank’s analysis indicates that India’s unemployment rate was 8.7% in March 2016. 43 months after September 2016 saw a high unemployment rate. This rate rose to 7.16 percent in January 2020. Farmer’s fields decreased by 14.53 percent outside of metropolitan areas and 13.08 percent inside them due to lockdown. The present crisis is distinct since it has increased in terms of length and unemployment rate. The two variables have never increased so quickly in such a short amount of time. Methodology: A precise phrase and a few selected phrases were used in a search query to exclude Central India from publications in the Web of Science Database. R-Studio Application was imported and looked through. Results: 68 papers from 180 sources were included in the main list of publications. Most of them were journal entries. There were more corresponding authors from India in addition to a few more authors from 51 other nations, such as Germany, France, Canada, India, and the United States. The Index for teamwork is 4.82. For publishing, the annual percentage growth rate was 0.959. The Indian Society of Labor Economics (ISLE) will no longer perform employment research, which is the most severe immediate result of the COVID-19 problem, according to a study. Other long-term effects include slower economic development and increased inequality. 520 ISLE participants completed the online survey, which was launched in the last week of May.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,086 | 0,179 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».