Ethics of COVID-19-related school closures
Bibliographic record
Abstract
COVID-19 mitigation strategies have led to widespread school closures around the world. Initially, these were undertaken based on data from influenza outbreaks in which children were highly susceptible and important in community-wide transmission. An argument was made that school closures were necessary to prevent harm to vulnerable adults, especially the elderly. Although data are still accumulating, the recently described complication, pediatric multisystem inflammatory syndrome, is extremely rare and children remain remarkably unaffected by COVID-19. We also do not have evidence that children are epidemiologically important in community-wide viral spread. Previous studies have shown long-term educational, social, and medical harms from school exclusion, with very young children and those from marginalized groups such as immigrants and racialized minorities most affected. The policy and ethical implications of ongoing mandatory school closures, in order to protect others, need urgent reassessment in light of the very limited data of public health benefit.
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 | 0.162 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".