A Strategic Path for Digital Transformation in Cyber Warfare for African Militaries
Bibliographic record
Abstract
Digital disruption has changed the battlefield and increased its complexity for the war fighter. The modern battlefield continues to increase this complexity, due to the evolution of components that constitute military capability. The technologies, processes and the users are such components. The modern battlefield relies on advanced technologies tapping on high connectivity, are more lethal, precise, and autonomous. Due to this evolution, areas once thought to be safe from conventional attacks are increasingly becoming vulnerable. This evolution of technology and shorter development curves have also increased the prominence of the cyberspace, as a domain of war. However, many militaries, especially in Africa are still operating legacy systems and struggling with modernizing their systems to take advantage of the digital evolution. This paper, therefore, uses a systematic literature review and benchmarking focusing on selected super cyber power nations’ indices to propose a strategic path for African militaries to drive digital transformation in their operational environments. The roadmap is proposed to stimulate the establishment and enhancement of African militaries’ cyber warfighting capabilities in the digital age. The objectives of this digital transformation path include establishing a digital backbone, where all the sensors, effectors and the deciders are plugged to share information and intelligence.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".