Look south: challenges and opportunities for the ‘rules of the road’ for cyberspace in ASEAN and the AU
Bibliographic record
Abstract
As the inaugural United Nations Open-Ended Working Group (UN OEWG) has not significantly updated nor advanced the ‘rules of the road’ for cyberspace, regional organizations such as the Association of Southeast Asian Nations (ASEAN) and the African Union (AU) provide additional venues wherein deliberations can continue among a smaller group of states. Several ASEAN and AU member states are also active participants at the UN OEWG. Nonetheless, questions remain on how and where agreement on international law and cyber norms at the regional level can be achieved. To assess the challenges and opportunities for progress, this paper examines the public positions of two ASEAN member states, Indonesia and Singapore, and two AU member states, Kenya and South Africa, during the 2019–2021 UN OEWG meetings and situates them in their respective regions. We argue that substantial progress at the regional level is challenging to achieve, due to varying attitudes and levels of technological development among states, long-standing concerns over state sovereignty, and the vital role that a highly motivated and well-resourced regional actor plays in championing the cause. Opportunities exist, however, in that ASEAN and the AU provide paths for leveraging existing partnerships on cybersecurity and building trust in the region.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".