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 …
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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 | 0.057 | 0.108 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.053 | 0.073 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".