Bibliographic record
Abstract
By the end of the first year of the COVID-19 pandemic, in February 2021, the numbers of cases and deaths in southern Africa were low in absolute and relative numbers. The BBC ran a story (which was later retracted) headlined “Coronavirus in Africa: Could poverty explain mystery of low death rate?”. A heading in the New York Post said: “Scientists can’t explain puzzling lack of coronavirus outbreaks in Africa”. Journalist Karen Attiah concluded: “It’s almost as if they are disappointed that Africans aren’t dying en masse and countries are not collapsing”. We wondered if the knowledge that southern African countries have acquired in their struggle against AIDS has contributed to a more effective approach against COVID-19. The viral origins of the diseases through zoonotic events are similar; neither has a cure, yet. In both diseases, behaviour change is an important prevention tool, and there are specific groups that are more vulnerable to infection. Equally, there are important differences: most people with COVID-19 will recover relatively quickly, while people living with HIV will need lifelong treatment. COVID-19 is extremely infectious, while HIV is less easily transmitted.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".