The Digital Silk Road: “Tech-Diplomacy” as a Paradigm for Understanding Technological Adoption and Emerging Digital Regulations in MENA
Bibliographic record
Abstract
Abstract The Belt and Road Initiative (BRI) is a Chinese infrastructure and investment project launched in 2013 that seeks to link China with over 70 countries through transport, communication, and trading networks. The BRI consists of building and construction projects including railways, ports, roads, and other vital trade infrastructure. Importantly, the BRI also includes the establishment of a new “Digital Silk Road” (DSR) enhancing digital connectivity using the latest 5G high bandwidth, low latency mobile and satellite technology. In 2019, several Western states (notably USA, Australia, Canada, and the UK) banned Chinese telecommunications technology companies, such as Huawei, from rolling out 5G networks in their respective jurisdictions. The purported basis for the bans were security concerns over the ability of the Chinese government to control and potentially intercept communications over the Huawei systems. In the MENA region, no such bans have been adopted and the DSR is proceeding to connect MENA economies to China at a rapid pace. This places MENA countries in a precarious position between strategic links with the US as the global hegemon with a strategic interest in the region, and the emerging Chinese global political and economic order. The regulation of digital communication technologies is one dimension where legal frameworks must be designed with care and discernment to balance competing geopolitical forces. This article seeks to answer the question of how best to understand the legal regulation of new technologies in the MENA region and argues that the conceptual lens of “Tech-diplomacy” helps to provide such an understanding. In addition to privacy-centric, security-centric, and growth-centric philosophical and jurisprudential approaches to understanding data regulation, the predicament of the MENA region is a case study in how geopolitics can also inform our understanding of tech regulation.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".