Protezione dei dati personali e diritti fondamentali della persona: le nuove norme sui "codici di prenotazione" (PNR)
Bibliographic record
Abstract
italiano1. Introduzione. - 2. I codici di prenotazione (PNR) e il loro impiego come strumento di prevenzione e contrasto nei confronti di atti di interferenza illecita. La dimensione internazionale ed europea. - 3. L' approccio del legislatore dell'Unione euorpea in tema di PNR e il quadro giuridico di riferimento. - 4. Il faticoso 'iter' legislativo della direttiva 2016/681/UE e i suoi condizionamenti esterni. - 5. La direttiva 2016/681/UE e il suo contenuto. - 6. Il test di necessita e di proporzionalita come parametro di legittimita delle limitazioni dei diritti e delle liberta del singolo. - 7. La giurisprudenza della Corte EDU. - 8. La giurisprudenza della Corte di giustizia. - 9. Considerazioni conclusive. EnglishIn the present paper, the Author deals with the problematic interaction between, on the one hand, the fundamental right to personal data protection and, on the other hand, the needs of national security, focusing in particular on the directive 2016/681/UE on the use of passenger name record (PNR) data for the prevention, detection, investigation and prosecution of terrorist offences and serious crime. The first part of the paper analyses the International legal framework on PNRs and provides an overview of the agreements concluded by the European Union and third countries (United States of America, Canada and Australia) on the transfer of such data between the two parties. The second part of the paper offers a critical analysis of some specific aspects of the directive 2016/681/UE with the purpose of verifying whether they are fully compliant with the fundamental rights expressly set forth in Article 8 of the European Convention of Human Rights and Articles 7 and 8 of the EU Charter of Fundamental Rights, as interpreted by the two Courts.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".