The ‘right to be forgotten’ beyond the EU: an analysis of wider G20 regulatory action and potential next steps
Bibliographic record
Abstract
It has been increasingly asserted that data protection can and should enable individuals to exert some control at least ex post over online data dissemination. Notwithstanding contrary suggestions, therefore, the ʻright to be forgottenʼ is not solely an EU phenomenon. Post-2014 the majority of the eight national DPAs operating in non-EU G20 jurisdictions with established data protection legislation have sought to implement such a right through guidance and, in three cases, also enforcement. These jurisdictions span three regions and encompass jurisdictions such as Australia and Canada with a similar outlook to the EU. In light of the profoundly globalised nature of the internet, greater transnational coordination would be valuable. Whilst the G20 is itself ill-suited to this task, the pan-regional Data Protection Convention framework overseen by the Council of Europe as well as the Global Privacy Assembly could play an important role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".