Setting global health research priorities
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
When the G8 countries met in Canada in 2002 the topics of security, health, and Africa figured prominently. The three issues are related. Africa's human health is reeling from HIV/AIDS and other infectious diseases, posing national and regional security risks. The continent's economic health is stagnant or eroding, the result of structural adjustment programmes,1 domestic conflicts, corruption, and deteriorating human health. Recognising the complexities of these entwined relations, the G8 Africa action plan included a commitment to support health research on diseases prevalent in Africa. How well G8 member nations—Canada, the United States, England, France, Germany, Italy, Japan, and Russia—abide by this commitment is a matter of time and lobbying efforts. But what form should this new health research investment take? Should it emphasise specific diseases affecting poor people most, as favoured by the Commission on Macroeconomics and Health of the World Health Organization?2 Should it heed the call of biotechnology researchers, who have tabled their list of “top 10” research investments for global health, which range from better …
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.014 | 0.013 |
| 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