Data Driven Innovation, Privacy and National Sovereignty in a Global World-Two 2018 Key Cases on Internet Jurisdition
Bibliographic record
Abstract
Introduction: Anyone who has been following the evolution of the law on Internet jurisdiction would have noticed the ebb and flow of developments. With decisions such as the Canadian Supreme Court's ruling in Google v Equustek, 1 the Court of Justice of the European Union's (CJEU) decision in Bolagsupplysningen OOZ and the Supreme Court of New South Wales' ruling in Xv Twitter,3 2017 signalled that we are in a period of serious flow. However, the cases of 201 7 pale in significance when viewed next to what we can expect from 2018. In 2018, we will get to experience the Supreme Court of the United States' hearing and decision in the much-anticipated MicrosoftWarrantcase.4 And we will also see the CJEU's hearing, and possibly decision, in the arguably even more important Google France case.5 I devote this contribution to an analysis of these two cases, both of which may significantly impact data driven innovation, privacy and national sovereignty in a global world.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| 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".