Bibliographic record
Abstract
On July 12, 2022, following the Russian invasion of Ukraine, the European Data Protection Board (EDPB) issued a warning to data exporters, reminding them Russia did not have an adequacy agreement governing cross-border data flows of Europeans’ personal data to Russia. As such, blanket transfers of personal data were not permissible under European data protection law; instead, compliance needed to be assessed by data exporters on a case-by-case basis, and, where it could not be ensured, transfers should be suspended. This article views the EDPB declaration as a shot across the bow and extrapolates it to a future where cross-border data flow restrictions are deployed as an instrument of cooperative security as well as deterrence and defense. Given the potential sensitivity of personal information being transferred across borders, along with the economic value inherent in data flows in the digital economy, restrictions on cross-border data flows have the potential to inflict serious harm. This article explores the broader implications of this potential practice, assessing its security opportunities and drawbacks. The article advocates for reforming North Atlantic Treaty Organization (NATO) members’ divergent approaches to the regulation of processing of cross-border data transfers; it suggests these member states can and should overcome their splintered approaches by establishing a “safe data zone” to facilitate cross-border data flows among members, where NATO retains the power to issue embargoes on cross-border data flows to specific jurisdictions while otherwise leaving decisional authority for transfers to supranational entities like the European Union (EU) or sovereign states. This approach would increase cross-border data flows between allies while permitting restrictions with adversaries where doing so achieves security objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".