Some further reflections on the Directive (EU) 2016/681 on PNR data in the light of the CJEU Opinion 1/15 of 26 July 2017
Bibliographic record
Abstract
espanolEn la ultima decada, ha surgido la necesidad de una mayor cooperacion entre las autoridades nacionales de los diferentes Estados para hacer un uso mas sistematico de los datos entre ellos para luchar contra el terrorismo y otros crimenes. El 21 de abril de 2016, el Consejo adopto la Directiva 2016/681 para regular la transferencia de los datos PNR de las lineas aereas a los Estados miembros, asi como el tratamiento de estos datos por las autoridades competentes. Su validez, en relacion con el equilibrio entre las necesidades de seguridad y el respeto de los derechos fundamentales, como el derecho al respeto de la vida privada y el derecho a la proteccion de los datos personales, podria ser impugnada como consecuencia de la opinion emitida por el TJUE sobre el acuerdo UE-Canada en relacion a la transferencia de datos personales. EnglishOver the last decades, it has arisen the need for increased cooperation between law enforcement authorities in making more systematic use of the data furnished by those moving to and from the States in order to prevent, detect, investigate and prosecute terrorism and other serious crimes. On 21 April 2016 the Council adopted Directive 2016/681 in order to regulate PNR data transfer from the airlines to the Member States, as well as the processing of this data by the competent authorities. Its validity, with particular reference to the balance between needs of security and the respect of fundamental rights, such as the right to respect for private life and the right to the protection of personal data, could be challenged after the conclusions reached by the CJEU in its Opinion on the EU-Canada agreement on PNR transfer.
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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 | 0.066 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.059 | 0.055 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".