Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has swept across China and the world, causing more than 30 million infections and incalculable damage. China was seriously damaged and threatened by the disease in the first quarter of 2020, but finally succeeded in halting its spread in a short period. This was achieved through quick and strong measures in self-protection, mobility control, resource allocation, professional health care, and disinfection, under the organization of the government and the cooperation of all the Chinese people. The measures that were taken to prevent the spread of COVID-19 proved to be efficient in fighting the outbreak in Beijing in June 2020. This paper reviews China's experience with COVID-19, the Chinese economy's performance during the pandemic, and the government's policies to protect lives, maintain markets, and promote the economy. The data show that strong monetary and fiscal policies accelerated the country's economic recovery. These policies, including tax reductions and credit support, targeting small- and medium-size enterprises (SMEs) and industries and regions that were severely damaged, have helped to create jobs and encourage production and investment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it