Building Research and Implementation Capacity among Early Career African Scientists
Bibliographic record
Abstract
Building the capacity of a new generation of scholars is both a necessary and an exciting quest. It is an opportunity to be intentional in passing on the baton of ‘know-how’ (knowledge and experience) and ‘know-do’ (competence and leadership) in a way that ensures that future generations of scholars will generate the scientific evidence to support policy and program decisions make the world a better place. This special issue of AJFAND includes output from scholars involved in capacity building activities that have been possible through more than 20 years of research and training partnership between the University of Ghana, McGill University, and rural institutions in Ghana. The two Universities have collaborated on multiple projects (the RIING, ENAM, Nutrition Links, and LInkINg UP projects) that have developed the capacity of young trainees while improving maternal and child nutrition in rural settings [1-3].
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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 | 0.091 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".