Shortcomings of the Passenger Name Record Directive in Light of Opinion 1/15 of the Court of Justice of the European Union
Bibliographic record
Abstract
By 25 May 2020, the European Commission is obliged to conduct a full review of the Passenger Name Record (PNR) Directive and provide a comprehensive report to the European Parliament and the Council on seven key aspects of the said Directive. These range from an assessment of the necessity and proportionality for collecting and processing PNR data in relation to each of the Directive’s purposes, to the length of the data retention period, and even the effectiveness of exchanging information among Member States, including statistical information on the number of passengers whose PNR data has been collected, exchanged or identified for further examination. The review could lead the European Commission to present a legislative proposal to amend the PNR Directive which could either reinforce, maintain or dilute the EU PNR system. More recently, two not-for-profit associations have legally challenged the national PNR schemes based on the PNR Directive. This paper questions the validity of certain provisions of the Directive in light of Opinion 1/15 of the Court of Justice of the European Union of 26 July 2017 concerning the EU-Canada PNR Agreement. It also calls on the European Commission, as guardian of the EU Treaties and of EU law, to conform the PNR Directive to the Luxembourg Court case-law on mass data retention schemes, taking advantage of the review momentum. Keywords: CJEU; Opinion 1/15; Directive 2016/681; data protection; PNR; law enforcement; data retention; Articles 7 and 8 of the Charter of Fundamental Rights
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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 | 0.076 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.033 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".