Sub-Saharan African Countries‘ COVID-19 Research: An analysis of the External and Internal Contributions, Collaboration Patterns and Funding Sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study aims at providing some evidence-based insight into Sub-Saharan Africa’s first eighteen months of COVID-19 research by evaluating its research contributions, patterns of collaboration, and funding sources. Eighteen months (2020 January 1-2021 June 30) COVID-19 publication data of 46 Sub-Saharan African countries was collected from Scopus for analysis. Country of affiliation of the authors and funding agencies data was analyzed to understand country contributions, collaboration pattern and funding sources. USA (23.08%) and the UK (19.63%), the top two external contributors, collaborated with Sub-Saharan African countries about three times more than other countries. Collaborative papers between Sub-Saharan African countries - without contributions from outside the region- made up less than five per cent of the sample, whereas over 50% of the papers were written in collaboration with researchers from outside the region. Organizations that are in the USA and the UK funded 45% of all the COVID-19 research from Sub-Saharan Africa. 53.44% of all the funding from Sub-Saharan African countries came from South African organizations. This study provides evidence that pan-African COVID-19 research collaboration is low, perhaps due to poor funding and lack of institutional support within Sub-Saharan Africa. This mirrors the collaborative features of science in Sub-Saharan Africa before the COVID-19 pandemic. The high volume of international collaboration during the pandemic is a good development. There is also a strong need to forge more robust pan-African research collaboration networks, through funding from Africa’s national and regional government organizations, with the specific objective of meeting local COVID-19 and other healthcare needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it