Bibliographic record
Abstract
There is a vast literature on COVID-19, and this update cannot hope to cover all of what is known about infection in children. Instead, the approach taken is to consider evidence in the light of what I will call ‘myths’ that predominated the pandemic narrative. Now that SARS-CoV-2 is an endemic virus, and much of the panic has subsided, it is important to revisit these myths in order to learn from our mistakes so that we do not repeat the same in the future. I will give evidence to show that SARS-CoV-2 was never a great threat to children, sequelae of infection in children were exaggerated, and vaccine safety and efficacy in children were exaggerated. Nevertheless, the response to the pandemic caused immense predictable and preventable harm to children. Better responses would have considered focused protection of those at high risk from the virus (i.e., older people with severe comorbidities), reducing fear in the population, augmenting surge capacity in healthcare, and cost-benefit analyses of possible responses (i.e., considering the predictable collateral damage acknowledged in previous literature). The Emergency Management process was not followed. This process should now be followed in devising a plan for recovery from the pandemic responses.
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 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".