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Record W2886869774 · doi:10.5944/rdp.102.2018.22395

El control de datos de circulación de personas en la UE como mecanismo de salvaguarda de la seguridad nacional

2018· article· es· W2886869774 on OpenAlexaboutno aff
Rosario Serra Cristóbal

Bibliographic record

VenueRevista de Derecho Político · 2018
Typearticle
Languagees
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesPersonaArt

Abstract

fetched live from OpenAlex

Resumen:La gestión coordinada de las fronteras y el funcionamiento eficaz de los sistemas de tratamiento de datos de circulación de personas pueden servir como mecanismo de alerta temprana frente al riesgo de ataques terroristas. Puede fortalecer la capacidad colectiva de los Estados para detectar, prevenir y combatir el terrorismo al facilitar el intercambio oportuno de información, permitiendo así adoptar de forma responsable decisiones cruciales.Este trabajo analiza los concretos instrumentos de gestión de datos en fronteras que pueden ser útiles en la lucha antiterrorista, porque el primer paso en inteligencia reside en la obtención de información, que luego será analizada y tratada para convertir esa información en conocimiento. Como tendremos oportunidad de comprobar, muchas de las bases de datos en fronteras se crearon para controlar la entrada de inmigrantes en las fronteras europeas, pero la información que ofrecen dichos sistemas puede servir también para luchar contra ese reto que nos amenaza, el del terrorismo yihadista. No obstante, este trabajo subraya que se trata de fenómenos distintos.Es cierto que la nueva oleada de ataques yihadistas ha coincidido, en el mismo espacio temporal, con la mayor crisis migratoria a la que se ha tenido que enfrentar Europa debido a crisis humanitarias y posteriormente a la guerra de Siria u otros conflictos. Pero, no son lo mismo. El terrorismo yihadista y la inmigración poco o nada tienen que ver, por mucho que se hayan querido vincular o se hayan pretendido justificar determinadas políticas contra la inmigración como algo necesario para luchar contra el terrorismo yihadista, con el fácil argumento de que frenando la inmigración se evita la entrada de potenciales terroristas en Europa.El trabajo advierte del riesgo de que la lucha contra el terrorismo sea utilizada para reforzar los controles de personas en las fronteras con el verdadero objetivo de frenar los flujos migratorios. Al tiempo, subraya la necesidad de que en dichos controles se sigan directrices y prácticas claras y se respeten plenamente las obligaciones que los Estados tienen de conformidad con el Derecho internacional, tal como ha recordado el Tribunal Europeo de Derechos Humanos y el Tribunal de Justicia de la Unión Europea. De hecho, no son pocos los casos en los que estos Tribunales han subrayado la relevancia indubitada de principios como la reserva de ley, la necesidad o la proporcionalidad como sustrato de la licitud de muchas medidas que incluyen el tratamiento de datos personales.Summary:1. Jihadist terrorism as a cross-border phenomenon. 2. The benefit of data exchange on crossing-borders in the Schengen area. 3. New guidelines on data processing and the safeguard of national security. 4. The register of passengers (The Personal Name Record or PNR). 5. When the data cross the external borders. The exchange of data with third countries. 5.1. The failed PNR Agreement with Canada and the EU Court of Justice’s standards regarding the transfer of passengers’ data. 5.2. The exchange of data with the United States. The EU-US Umbrella Agreement and the Privacy Shield. 6. The use of profiles and blacklists of alleged terrorists in cross-bording. 7. ConclusionsAbstract:EU Coordinated border management and effective functioning of data processing systems related to the movement of persons may serve as an early warning mechanism against the risk of terrorist attacks. It can strengthen the collective capacity of States to detect, prevent and combat terrorism by facilitating the timely exchange of information, thereby enabling crucial decisions to be adopted in a responsible manner.This paper analyzes the concrete border data management tools that can be useful in the fight against terrorism. The first step in intelligence lies in obtaining information, which will then be analyzed and treated to turn that information into useful knowledge. As we will have an opportunity to verify, numerous border databases were created to control the entry of immigrants into European borders, but the information offered by these systems can also serve to fight against this challenge that threatens us, that of jihadist terrorism.Nevertheless, we emphasize that terrorism and immigration are different phenomena. The truth is that the new wave of Jihadist attacks took place along the largest migratory crisis that Europe faced due to different humanitarian crises and to the war in Syria and other conflicts. But they represent different realities. Jihadist terrorism and immigration have little or nothing in common. In spite of this, many wish to link both with a view to justify certain anti-immigration policies as necessary actions for coping with Jihadist terrorism. This has been done based on a simple narrative: holding back immigration prevents the entry of potential terrorists in Europe.This paper shows that the risk that the fight against terrorism will be used as a basis to reinforce people controls at the borders, while the true objective of these measures is to curb migratory flows. At the same time, it underlines the need for clear guidelines and practices to be followed when implementing such controls. It also vindicates the need for States to observe their obligations laid down by international law, as recalled by the European Court of Human Rights and the EU Court of the Justice. In fact, in many cases, these jurisdictions highlighted the undoubted relevance of the statutory reserve principle, the principle of necessity or the principle of proportionality, as legal basis for the adoption of measures that include personal data processing.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.013
GPT teacher head0.336
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
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