The Africa Center for Biostatistical Excellence: a proposal for enhancing biostatistics capacity for sub‐Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa has a shortage of well-trained biomedical research methodologists, in particular, biostatisticians. In July 2014, a group of biostatisticians and researchers from the region attended a brainstorming workshop to identify ways in which to reduce the deficit in this critical skill. The workshop recognized that recommendations from previous workshops on building biostatistics capacity in sub-Saharan Africa had not been implemented. The discussions culminated with a proposal to setup an Africa Center for Biostatistical Excellence, a collaborative effort across academic and researcher institutions within the region, as a vehicle for promoting biostatistics capacity building through specialized academic masters programs as well as regular workshops targeting researchers.
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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 | 0.119 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.035 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".