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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it