Reconfiguring Data Infrastructure Ecosystem in Africa: A Primer Toward Digital Sovereignty
Bibliographic record
Abstract
The growth of the Internet and its associated technologies; including digital services have tremendously impacted our society. However, scholars have noted a trend in data flow and collection; and have alleged mass surveillance and digital supremacy. To this end therefore, nations of the world such as Russia, China, Germany, Canada, France and Brazil among others have taken steps toward changing the narrative. The question now is, should Africans join these giants in this school of thought on digital sovereignty or fold their hands to remain on the other side of the divide? This question among others are the main reasons that provoked the thoughts of putting this paper together. This is with a view to demystifying the strategies to reconfigure data infrastructure in the context of Africa. It also highlights the benefits of digital technologies and its propensity to foster all round development in the continent as it relates to economic face-lift, employment creation, national security, among others. There is therefore a need for African nations to design appropriate blueprint to ensure security of her digital infrastructure and the flow of data within her cyber space. In addition, a roadmap in the immediate, short- and long-term in accordance with the framework of African developmental goals should be put in place to guide the implementation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".