Regulating cross-border data flows and privacy in the networked digital environment and global knowledge economy
Bibliographic record
Abstract
As our lives continue to shift online and as smarter network-enabled technologies continue to blur the line between real and virtual, the threat to privacy is unmistakably real. While the Internet has become a convenient part of the social fabric of everyday life, it is challenging national privacy regulation founded upon state sovereignty, based as it is upon physical borders. In an interconnected world, the issue of privacy has indisputably become one of international flavour. The question at the heart of this article is how does the environment over which the law seeks to be effective affect the rule of law. Specifically, the article examines how the networked digital environment and the global knowledge economy affect the ability of a small selection of regulatory schemes to effectively address cross-border data flows and privacy.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".