Developing Robust Cyber Warfare Capabilities for the African Battlespace
Bibliographic record
Abstract
The evolution of technology in the African battlespace continues to pose a significant challenge to the African militaries. This evolution increases the need for the African militaries to be able to operate in the cyberspace strategically and effectively. Developing cyber warfare capabilities remains a challenge to many African militaries who are struggling to remain afloat due to ever decreasing resources, including budgets. This in turn reduces the effect of these militaries in the evolving battlespace. This paper seeks to present a comprehensive framework for developing cyber warfare capabilities for African militaries to be able to operate efficiently in the cyber battlespace. The proposed POSTEDFIT aligned framework, requires a comprehensive system thinking approach towards developing capabilities in a phased manner. This includes the ability to define the capabilities in terms of the requirements presented by the cyberspace, and the components forming these capabilities. The generic framework is based on the basic understanding of a capability, as the ability to do something, in this case, the ability to secure and operate in the cyberspace for African militaries, ability to conduct offensive cyber operations and ability to keep abreast with the evolving cyber battlespace.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".