Bibliographic record
Abstract
Covid-19 is a serial killer. In less than two years it has taken the lives of over five million people. It preys on the vulnerable and the elderly. Seniors in long term care facilities are a favorite target. Governments have reacted differently to the threat. Some, including China and Australia, have adopted an aggressive, no- nonsense approach, locking down major cities for months at a time. Others, including Sweden and Brazil were, at least initially, more restrained and laissez faire, allowing their citizens to move about freely and letting the virus run its natural course. Countries also differed in the extent to which the general public was engaged in deciding which approach to adopt. In some the public were very active; in others not at all. In China, public debate and criticism were prohibited. Decisions were made by senior members of the Communist Party from behind closed doors and policies were presented as a fait accompli. In Europe and the United States, members of the general public were much more vocal and outspoken. Citizens who disagreed with their governments organized large protests and demonstrations and, when they were not listened to, took their political masters to court.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.065 |
| Scholarly communication | 0.032 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 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".