Mutual Pan-African support paradigm to produce scientific evidence of traditional medical practices for use against COVID-19 and emerging pandemics
Bibliographic record
Abstract
Africa is endowed with a profoundly rich and diverse system of plants and other bio-resources out of which, by traditional medicine practice, the people have satisfied their healthcare needs right from antiquity. In contemporary times, it has become necessary to modernize this traditional medical care system via scientific studies. Validation of the efficacy of health-enhancement products and drugs from plants and other bio-resources is predicated on diligent and intensive research accompanied by rigorous and conclusive clinical trials. Africa has eminently qualified human resources but due to the finance-intensive nature of medical research, individual African states on their own cannot fund the level of research desired for dealing with such serious issues as the COVID-19 pandemic. A collaboration among African states guided by a Mutual Pan-African support paradigm (MPASP) is a unique strategy for achieving success in any such a high-impact global project as the use of traditional medicine against COVID-19 and emerging pandemics; and this is hereby advocated.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".