Informing children citizens efficiently to better engage them in the fight against COVID-19 pandemic
Bibliographic record
Abstract
Since the beginning of the year, the world's attention has rightly been focused on the spread of the Coronavirus Disease 2019 (COVID-19) pandemic and the implementation of drastic mitigation strategies to limit disease transmission. However, public health information campaigns tailored to children are very rare. Now more than ever, at a time when some governments are taking populations out of lockdown and youth are returning to schools, children around the world need to fully grasp the modes of transmission of the disease, the health risks, the scientific notions of the immune system, the value of barrier measures, and the progress of scientific research. In the context of the COVID-19 pandemic, comics can be very useful for communicating quickly and effectively abstract and important information to children who might be under the influence of a large amount of sometimes contradictory information. Conveying precise, reliable, and accessible information to children is key in a world overwhelmingly impacted by the outbreak. This should be the role and the responsibility of world health official leaders and governments in compliance with the United Nations Convention on the Rights of the Child. In partnership with mainstream medias, consortia of scientists, communication experts, and education specialists, it is urgent that world leaders engage children in this worldwide public health fight.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".