“Vaccinate every child against COVID-19”
Bibliographic record
Abstract
The global pandemic of COVID-19 has created havoc worldwide. By the first week of December 2021, 0.26 billion COVID-19 infected cases and 5.2 million deaths have been reported globally.[1] United Nations Children Fund (UNICEF) reports that more than 10,000 children and adolescents have died from COVID-19, with a case fatality rate of 0.3%. Out of 299 vaccine candidates, 28 are available to the general population in less than 1 year.[2] For children, WHO permitted vaccine Pfizer/BioNTech, Sinovac, and Sinopharm, Drug Controller General of India's approved ZyCov-D and Covaxin, and the Cuban government approved Soberna 2, and Soberna plus are available.[3] Italy, Germany, France, Norway, Switzerland, Israel, Dubai, Japan, Canada, and the US have already started vaccinating their children. This step may decrease the transmissibility and mutations of the virus and thus restore normalcy. For India, it is a question of "To be or not to be?" Indian researchers have warned of the long-term impact of the pandemic on the health, development, learning, and behavior of children, thus pushing the agenda of vaccination and opening of schools. All attempts at opening schools have failed in the last 2 years. Vaccinating children is not easy as it has taken nearly 1 year to vaccinate half of the adult Indian population. In these circumstances, rather than "vaccine for all," "vaccine for (chronically) ill" is the only feasible solution for children.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".