The Protection of Personal Data in the Fight Against Terrorism: New Perspectives of Pnr European Union Instruments in the Light of the Treaty of Lisbon
Bibliographic record
Abstract
This article addresses the protection of personal data in the European Union in the context of the fight against international terrorism taking into account the new European Union architecture provided for by the Treaty of Lisbon. After having delineated the European legal background concerning the right to privacy, the author examines the Passenger Name Records (PNR) Agreements concluded by the European Union with the United States, Canada and Australia. A further object of analysis is the 2007 proposal for a Council Framework Decision on the use of PNR data for law enforcement purposes, which is aimed at creating an autonomous PNR system in the European Union. The author considers that these instruments are likely to violate rights and fundamental freedoms of individuals, in particular the right to privacy. This is also due to the architecture of the European Union, whose structure is incapable of adequately and completely protecting the right to personal data protection. As a consequence, the author proposes solutions to modify PNR instruments, especially in light of the future changes that the Treaty of Lisbon will make to the structure of the European Union.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.036 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.019 | 0.013 |
| 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".