Notice bibliographique
Résumé
Introduction to the special issueThe COVID-19 pandemic, caused by the SARS-CoV-2 virus, was first detected in late 2019 in Wuhan, China, and has since posed a severe threat to global public health.It spread quickly between people and continents and has continued into 2023, with the emergence of new variants posing a significant challenge to containment efforts.The virus's origins and characteristics were initially unknown, causing concern and a feeling of unpreparedness among scientists, governments, and non-governmental organisations (NGOs) worldwide as they struggled to grasp the enormity of the situation and find ways to lessen its effects (Hu et al., 2021; United Nations, 2020).As it turned out, the pandemic's effects have been far-reaching, with governments in Asia being particularly impacted by a lack of information and resources required to address its numerous challenges.It resulted in the loss of approximately seven million lives globally and had a significant impact on the economic, social, and political spheres altering the fabric of daily life in countless ways.To combat the pandemic's destabilising effects, governments and institutions have had to quickly mobilise and adapt to a constantly shifting, often overwhelming, situation (OECD, 2020;Rodrigues and Plotkin, 2020).Through cooperative efforts between the public and private sectors, the state can be freed of some of the burdens of crisis relief (Park and Chung, 2021).According to the World Health Organization, a pandemic occurs when a newly discovered disease spreads rapidly across the globe.The United States Centers for Disease Control and Prevention define a pandemic as a worldwide epidemic caused by the rapid spread of a newly emerging infectious virus.Interestingly, pandemics hit approximately every hundred years.The plague outbreak took place in 1720, a cholera epidemic in 1817, and the Spanish flu in 1918, followed by the coronavirus in 2019 (Kertscher, 2020).The advent of the COVID-19 pandemic necessitated a rapid and comprehensive response from the scientific community and governments, as they sought to contain the rapidly evolving virus through the development and dissemination of effective vaccines.In parallel, governments endeavoured to craft evidence-based public health policies to manage the pandemic, responding to emergency situations with restrictions and lockdowns while mitigating the negative societal and economic impacts of such measures (OECD, 2021).Although preventive measures such as vaccination and isolation of affected individuals were immediately prioritised, the pandemic highlighted the importance of strategic planning and coordination between governments and the private sector in responding to the crisis (Buse, 2004).However, new infections and restrictions continued to impose a substantial strain on the economies of several countries, despite ongoing efforts to curb the spread of the virus and alleviate its impact.The pandemic cannot be effectively managed without continuous scientific research, an evidence-based approach to policymaking, and the coordination and cooperation of multiple stakeholders, including governments, businesses, and civil society.These measures are
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,001 | 0,001 |
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 ».