EU exclusive jurisdiction on surveillance related to terrorism and serious transnational crime, case review on opinion 1/15 of the CJEU
Bibliographic record
Abstract
This case review examines the Opinion by the Court of Justice of the EU (CJEU) published on 26 July 2017 on the legal basis of an international agreement signed between the EU and Canada providing for the transfer of information on passengers taking flights from the EU to Canada and the compatibility of that Agreement with the EU fundamental rights, particularly the rights to respect for private life and to protect personal data. Rather spectacularly, the CJEU struck down the Agreement, finding the legal basis inadequate and the terms incompatible with the EU privacy and data protection guarantees. This case review starts by providing a backdrop for the conclusion of the PNR Agreement concerned and for the CJEU’s Opinion on that Agreement. Then it moves on to analysing the procedural and substantive aspects of the Opinion, and finishes with an analysis of the consequences regarding counterterrorism measures and for the future of the exchange of personal data in the field of fight against terrorism and serious transnational crime. It is argued here that although Opinion 1/15 deviated from the standard of judicial review established by the CJEU in Digital Rights Ireland, Schrems, and Tele2, it should be considered as a step towards protecting the EU fundamental rights owing to the tight procedural conditions for the legality of data sharing agreements it introduced and the high risk of litigation following it.
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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 | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".