Bibliographic record
Abstract
‘Shielding’ and ‘isolating’ were the strictest forms of staying home. Four million people who were particularly vulnerable were asked to ‘shield’ at home for months, initially to protect the health service and latterly for their own protection. At least 12 million who had COVID-19 or suspected COVID-19 were required by law to ‘isolate’ at home to protect others (at least those outside their household). A quarter of shielders and isolators lived alone and became a new category of dependent people. Those who lived with others had to try to avoid infection at home. People on lower incomes were more likely to have to shield or isolate, but they and ethnic minorities were less likely to have a spare bedroom to do so properly, which must have contributed to inequalities in infection and death. Shielding and isolating were only partially successful. In 2020/21, there were 28 million or 18% fewer hospital appointments than before the pandemic. Millions were providing healthcare for household members with COVID, while trying to avoid infection, or conditions that would normally be treated in hospital. Some 34% more people than usual died at home. This placed extra responsibility on sick and vulnerable people, their households and homes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".