Tackling COVID-19: Can the African continent play the long game?
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
Events have progressed with dizzying rapidity since the World Health Organization (WHO) was first alerted to cases of severe pneumonia in the Wuhan City of China on December 31st 2019. The novel SARS-CoV-2 coronavirus disease (COVID-19) was declared a pandemic on March 11th 2020. As of April 7th, a total of 1.38 million cases of COVID-19 had been diagnosed globally with over 78 000 deaths attributable to the disease [1]. Comparisons have been drawn between COVID-19 and other deadly pandemics such as the 1918 Spanish flu that infected about one-third of the world’s population, killed 40-50 million people and changed the course of history [2]. While it is premature to judge the final death toll of COVID-19, the global response to the pandemic will determine how bad it becomes.
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 | 0.003 | 0.018 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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