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Record W3013801325 · doi:10.21552/edpl/2020/1/9

Shortcomings of the Passenger Name Record Directive in Light of Opinion 1/15 of the Court of Justice of the European Union

2020· article· en· W3013801325 on OpenAlexaboutno aff
Sara Roda

Bibliographic record

VenueEuropean Data Protection Law Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsnot available
Fundersnot available
KeywordsDirectivePolitical scienceEuropean unionLawParliamentLegislatureDirective on Privacy and Electronic CommunicationsCommissionData Protection DirectiveProportionality (law)BusinessEuropean Union lawInternational tradeComputer sciencePolitics

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.271
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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