A Tale of Two Privacy Laws: The GDPR and the International Right to Privacy
Bibliographic record
Abstract
The European Union's General Data Protection Regulation (GDPR) is widely viewed as setting a new global standard for the protection of data privacy that is worthy of emulation, even though the relationship between the GDPR and existing international legal protections for the right to privacy remain unexplored. Correspondingly, this essay examines the relationship between these two bodies of law, and finds that the GDPR's provisions are neither necessary nor sufficient to protect the right to privacy as enshrined in Article 17 of the International Covenant on Civil and Political Rights (ICCPR). It argues that there are other equally valid and effective approaches that states can pursue to protect the right to privacy in an increasingly digital world, including the much-maligned American approach of regulating data privacy on a sectoral basis.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".