Developing a European Standard for International Data Transfers after Snowden: <i>Opinion 1/15</i> on the EU‐Canada PNR Agreement
Bibliographic record
Abstract
Abstract In Opinion 1/15 the Court of Justice of the European Union (CJEU) held that the proposed EU‐Canada Passenger Name Record (PNR) agreement must be revised because parts of it are incompatible with the EU fundamental rights framework. This note argues that the significance of Opinion 1/15 can only be understood in the broader historical context of increasing international securitisation between the 9/11 attacks in 2001 and the Snowden revelations in 2013. Opinion 1/15 emerges as a powerful addition to the existing data privacy trilogy established by the CJEU in the post‐Snowden era in an attempt to re‐balance the terms of international cooperation in data‐sharing between the EU and other countries. These terms were largely modelled around national security interests that have gained significant prominence in the aftermath of 9/11. While pro‐securitisation policies have been successful in gaining support among private and public actors, it is doubtful whether the CJEU pushback – without political support from EU Commission and Member States ‐ will achieve similar success.
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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 | 0.081 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.023 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".