First Universities Allied for Essential Medicines (UAEM) Neglected Diseases and Innovation Symposium
Bibliographic record
Abstract
Universities Allied for Essential Medicines organized its first Neglected Diseases and Innovation Symposium to address expanding roles of public sector research institutions in innovation in research and development of biomedical technologies for treatment of diseases, particularly neglected tropical diseases. Universities and other public research institutions are increasingly integrated into the pharmaceutical innovation system. Academic entities now routinely undertake robust high-throughput screening and medicinal chemistry research programs to identify lead compounds for small molecule drugs and novel drug targets. Furthermore, product development partnerships are emerging between academic institutions, non-profit entities, and biotechnology and pharmaceutical companies to create diagnostics, therapies, and vaccines for diseases of the poor. With not for profit mission statements, open access publishing standards, open source platforms for data sharing and collaboration, and a shift in focus to more translational research, universities and other public research institutions are well-placed to accelerate development of medical technologies, particularly for neglected tropical diseases.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".