The centrality of cybersecurity to socioeconomic development policy: A case study of cyber-vulnerability at South Africa’s Transnet
Bibliographic record
Abstract
Using South African state-owned enterprise (SOE) Transnet as a case study, this article explores the factors that influence the cybersecurity risks that are posed to infrastructure, with implications for markets and society, by advanced computational systems. We studied the legislation and corporate governance decisions leading up to the July 2021 breach of Transnet’s IT network, a high-profile event with potential cascading consequences. We also examined the evolution, since the country’s transition to democracy, of the South African government’s approach to fostering a developmental state. The findings illustrate that cybersecurity policy needs to be a core dimension of contemporary South African socioeconomic development policy, necessitating a central role for the developmental state in creating trusted marketplaces and procuring suitable security software systems. The findings also underscore the reality that a failure to act against increasing cyber-threats constitutes a substantial risk to the functioning of the South African market. Based on the findings, this article argues for a close examination of how the cybersecurity performance of South African SOEs can be improved. While focused on South Africa, the findings are relevant to other countries seeking to integrate robust cybersecurity measures into their national logistical and infrastructural sectors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".