Effective Infodemic Management: A Substantive Article of the Pandemic Accord
Bibliographic record
Abstract
Social media has proven to be valuable for disseminating public health information during pandemics. However, the circulation of misinformation through social media during public health emergencies, such as the SARS (severe acute respiratory syndrome), Ebola, and COVID-19 pandemics, has seriously hampered effective responses, leading to negative consequences. Intentionally misleading and deceptive fake news aims to harm organizations and individuals. To effectively respond to misinformation, governments should strengthen the management of an "infodemic," which involves monitoring the impact of infodemics through social listening, detecting signals of infodemic spread, mitigating the harmful effects of infodemics, and strengthening the resilience of individuals and communities. The global spread of misinformation requires multisectoral collaboration, such as researchers identifying leading sources of misinformation and superspreaders, media agencies identifying and debunking misinformation, technology platforms reducing the distribution of false or misleading posts and guiding users to health information from credible sources, and governments disseminating clear public health information in partnership with trusted messengers. Additionally, fact-checking has room for improvement through the use of automated checks. Collaboration between governments and fact-checking agencies should also be strengthened via effective and timely debunking mechanisms. Though the Intergovernmental Negotiating Body (INB) has yet to define the term "infodemic," Article 18 of the INB Bureau's text, developed for the Pandemic Accord, encompasses a range of actions aimed at enhancing infodemic management. The INB Bureau continues to facilitate evidence-informed discussion for an implementable article on infodemic management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".