Data Localisation in China and Other APEC Jurisdictions
Bibliographic record
Abstract
Data localisation provisions are becoming commonplace around the world, not just in Russia. In many of these countries, local data protection laws may require that certain categories of data must be stored and processed on local servers within the country. Such provisions may require that some or all categories of personal data may only be stored and processed on local servers, or they make their export subject to conditions. Both types of provision may be called ‘data localisation’. Such laws are controversial. The proposed Trans-Pacific Partnership (TPP) treaty between some APEC member countries includes onerous requirements on any Parties which have (or are considering) data localisation laws. The focus of this article is the data localisation requirements which are now emerging in China, an APEC member even though it has not proposed to become a party to the TPP. As yet, China's data localisation laws are only sectoral. Another version may soon be enacted in the Cybersecurity Law (nearing finalisation), which requires that “critical information infrastructure” (“CII”) providers to store “citizens’ personal information and important business data” within China unless their business requirements require overseas storage and they have passed a security assessment regarding such storage and transfer. Such a provision will have significant implications for many foreign businesses operating in China.Among APEC jurisdictions, China is not alone in adopting data localisation requirements. As well as the obvious example of Russia’s very sweeping law, they are found in at least Indonesia and Vietnam in very general forms, and in Canada and Australia in sector-specific forms. These are also explained in this article.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".