PNR: Passenger Name Record, Problems Not Resolved? The EU PNR Conundrum After Opinion 1/15 of the CJEU
Bibliographic record
Abstract
The long-standing debate concerning the transfer, processing and retention by national law enforcement authorities of Passenger Name Record (PNR) data has regained momentum with the adoption of Directive 2016/681/EU, which lays down a PNR regime operating within the EU, and, above all, with the delivery, on 26 July 2017, of the CJEU’s negative Opinion on the new envisaged EU-Canada PNR agreement. The Court’s finding that several provisions of the draft agreement do not comply with Articles 7 and 8 of the EU Charter of Fundamental Rights, on the protection of private life and personal data, inevitably raises doubts concerning the fate of the EU PNR bilateral agreements already in force (namely, with Australia and the United States) and of the PNR Directive. At the same time, this evolving scenario has immediate and very practical implications for air-carriers operating between the EU and third States, which may find themselves trapped by conflicting obligations due to the complex interplay between EU data protection laws, the EU PNR regime, and third States’ PNR legislation. Far from being limited to the EU legal order, the recent developments may exert an effect on foreign airlines’ operations to and from the EU and condition future negotiations between the EU and third countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.049 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".