Updating the case studies of the political economy of science granting councils in Sub-Saharan Africa
Bibliographic record
Abstract
This study, Updating the Case studies of the Political Economy of Science Granting Councils in sub-Saharan Africa, is a follow-up (Phase 2) to the case studies of the Political Economy of Science Granting Councils (SGCs) in sub-Saharan Africa research completed in 2017 (Phase 1, or baseline study). The study supports the Science Granting Councils Initiative (SGCI) in sub-Saharan Africa (SSA), funded by Canada’s International Development Research Centre (IDRC), the UK Department for International Development (DFID) and South Africa’s National Research Foundation (NRF). In the interest of generating evidence that can be deployed for economic and social development, the SGCI supports SGCs in 15 SSA countries. This research has been commissioned in response to an increasing recognition of the importance of improving understanding of the political economy (PE) of science and research in Africa and the roles that science, technology and innovation (STI)1play in the processes involved.The aims of the SGCI are to strengthen the capacity of SGCs to: manage research; design and monitor research programmes based on the use of robust STI indicators; support exchange of knowledge with the private sector; and establish partnerships among SGCs, and with other science system actors.
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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 | 0.035 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".