The Cyberspace ‘Great Game’. The Five Eyes, the Sino-Russian Bloc and the Growing Competition to Shape Global Cyberspace Norms
Bibliographic record
Abstract
The development of global norms of responsible state behaviour in cyberspace has, over the past decade, become a significant foreign policy issue and a new battleground between states. The contested and competitive nature of global cyberspace norm building suggests that although there are complicated legal and technical issues at play, the development of cyberspace norms remains primarily a contestation of values, ideologies, and strategic interests. This paper argues that the competition to shape the governance of cyberspace through the development of cyberspace norms represents a continuation of foreign and strategic policy applied to the cyber domain. This has resulted in a growing cyberspace `Great Game' between the Five Eyes alliance countries (the United States, United Kingdom, Australia, Canada, and New Zealand) and the Sino-Russian bloc (China and Russia). The Five Eyes and the Sino-Russian bloc are key cyber powers and cyberspace norm entrepreneurs whose leadership is instrumental in promoting global cyberspace norm preferences. However, each camp advocates a set of norm preferences inherently at odds with the other's, which has resulted in growing competition for dominance in cyberspace norm prescription and promotion. The paper outlines the key cyberspace norm proposals and initiatives promoted by the Five Eyes and the Sino-Russian bloc, discussing their main differences. It argues that the latest round (2019-2021) of the United Nations Group of Governmental Experts on Advancing Responsible State Behaviour in Cyberspace (UNGGE) deliberations is unlikely to help bridge these differences in any substantive way. The cyberspace `Great Game' and the increasingly competitivenature of cyberspace norm development will remain a feature of global efforts to govern cyberspace throughout the 2020s.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".