Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".