PNR and SWIFT agreements : external relations of the EU on data protection matters
Bibliographic record
Abstract
Since the 9/11 attacks there has been a dramatic increase in measures adopted in order to prevent and to combat international terrorism, which has had an impact on the existing data protection framework within the EU. This study will focus on the analysis of the international agreements signed between the EU and third countries regarding data transfers. In particular, PNR Agreements as well as the SWIFT Agreements will be examined, and I will also analyse the interconnection between the internal and external dimensions in depth, focusing on their mutual impact. In order to do this, an analysis and comparison of the current EU-US PNR Agreement, EU-Australia PNR Agreement and EU-Canada PNR Agreement will be carried out first. After, I will study the future European PNR Directive and possible implications for current PNR Agreements. I will then examine SWIFT and SWIFT II Agreements, paying special attention to the enhanced powers of the EP. At this point, it will be necessary to study the European Terrorist Finance Tracking System project as part of the EU Internal Security Strategy. Finally, concerning the negotiations recently opened by European Union and the United States on an agreement to protect personal information exchanged in the context of fighting crime and terrorism, I will examine this potential international agreement on data transfers between the EU and the US, and its impact on the rest of international agreements with regard to data protection.
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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 | 0.043 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".