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Introduction

However, data access issues pose a significant barrier for scientists to work on such kinds of legal data. The Convention itself entered into force in Yahoo search. The linear SVM has a regularisation parameter of the error term C , which is tuned using grid-search.

02/10/ · Télécharger l'étude. 3 de la Convention Européenne des Droits de l’Homme (CEDH) qui dispose que «Nul ne peut être soumis à la torture ni à des peines ou traitements inhumains ou dégradants» est peu à peu devenu la pierre de touche de la politique pénale des États adhérents à la Convention.

Rights And Freedoms - Lexinter Law

21/07/2020 · The preceding provision does not authorize any derogation from article 2, except in the case of death resulting from lawful acts of war, and from articles 3, 4 (paragraph 1) and 7. 3. Any High Contracting Party which exercises this right of derogation shall keep the Secretary General of the Council of Europe fully informed of the measures taken and of the reasons which have inspired them.

DETENTION ET ACTE INHUMAIN OU DEGRADANT : ARTICLE 3 DE LA CEDH

Cependant, l’article 3 ne prévoit pas de restrictions, ce en quoi il contraste avec la majorité des clauses normatives de la Convention et, conformément à l’article 15 § 2, il ne souffre nulle dérogation, même en cas de danger public menaçant la vie de la nation (Labita c. Italie [GC], n o 26772/95, § 119, CEDH 2000‑IV, Selmouni ...

Guide on 3 of Protocol No. 1 – Right to free elections European Court of Human Rights 2/35 Last update: Publishers or organisations wishing to translate and/or reproduce all File Size: KB.

Normativa de Derechos Humanos. Tabla normativa DDHH Sistema de Naciones Unidas :. Sistema Europeo :. Carta Social Europea CSE. Sistema Americano :. Sistema Africano :. Carta Africana sobre Derechos Humanos y de los Pueblos CAFDHP. Consejo de Derechos Humanos. Procedimientos especiales. Tribunal Europeo de DDHH TEDH. Tribunal de Justicia de la UE TJUE.

Second, the Court cannot openly acknowledge any kind of bias on its part. As a result, for example, clear displays of impartiality, such as failing to mention certain crucial events, seem rather improbable.

Last, we hasten to add that the above three kinds of considerations do not logically entail that other forms of non-outright or indirect bias in the formulation of facts are impossible.

Relevant law: This subsection of the judgment contains all legal provisions other than the articles of the Convention that can be relevant to deciding the case. The law: The law section considers the merits of the case, through the use of legal argument.

Depending on the number of issues raised by each application, the section is further divided into subsections that examine individually each alleged violation of some Convention article see below. Alleged violation of article x : Each subsection of the judgment examining alleged violations in depth is divided into two sub-sections.

The second one comprises the arguments made by the Court itself on the Merits. Merits: This subsection provides the legal reasons that purport to justify the specific outcome reached by the Court. Typically, the Court places its reasoning within a wider set of rules, principles and doctrines that have already been established in its past case-law and attempts to ground the decision by reference to these.

Operative provisions: This is the section where the Court announces the outcome of the case, which is a decision to the effect that a violation of some Convention article either did or did not take place. We create a data set 6 consisting of cases related to Articles 3, 6, and 8 of the Convention.

We focus on these three articles for two main reasons. Second, it is of crucial importance that there should be a sufficient number of cases available, in order to test the models.

Cases from the selected articles fulfilled both criteria. Table 1 shows the Convention right that each article protects and the number of cases in our data set.

For each article, we first retrieve all the cases available in HUDOC. Then, we keep only those that are in English and parse them following the case structure presented above. We then select an equal number of violation and non-violation cases for each particular article of the Convention.

Then, we choose all the cases in the smaller class and randomly select an equal number of cases from the larger class. Finally, we extract the text under each part of the case by using regular expressions, making sure that any sections on operative provisions of the Court are excluded. In this way, we ensure that the models do not use information pertaining to the outcome of the case.

We also preprocess the text by lower-casing and removing stop words i. We derive textual features from the text extracted from each section or subsection of each case.

These are either N-gram features, i. In a BOW model, a document or any text is represented as the bag multiset of its words unigrams or N-grams without taking into account grammar, syntax and word order. Each feature represents the normalized frequency of a particular N-gram in a case or a section of a case. We extract N-gram features for the Procedure Procedure , Circumstances Circumstances , Facts Facts , Relevant Law Relevant Law , Law Law and the Full case Full respectively.

Note that the representations of the Facts is obtained by taking the mean vector of Circumstances and Relevant Law. In a similar way, the representation of the Full case is computed by taking the mean vector of all of its sub-parts. Topics: We create topics for each article by clustering together N-grams that are semantically similar by leveraging the distributional hypothesis suggesting that similar words appear in similar contexts.

Using this vector representation of words, we compute N-gram similarity using the cosine metric and create an N-gram by N-gram similarity matrix. We finally apply spectral clustering von Luxburg, —which performs graph partitioning on the similarity matrix—to obtain 30 clusters of N-grams. Given that the obtained topics are hard clusters, an N-gram can only be part of a single topic. The problem of predicting the decisions of the ECtHR is defined as a binary classification task.

Our goal is to predict if, in the context of a particular case, there is a violation or non-violation in relation to a specific Article of the Convention. For that purpose, we use each set of textual features, i. The linear SVM has a regularisation parameter of the error term C , which is tuned using grid-search.

For Articles 6 and 8, we use the Article 3 data for parameter tuning, while for Article 3 we use Article 8. We compute the predictive performance of both sets of features on the classification of the ECtHR cases. Performance is computed as the mean accuracy obtained by fold cross-validation. V and NV represent the total number of cases where there is a violation or not respectively. Table 2 shows the accuracy of each set of features across articles using a linear SVM.

In general, both N-gram and topic features achieve good predictive performance. Our main observation is that both language use and topicality are important factors that appear to stand as reliable proxies of judicial decisions.

Therefore, we take a further look into the models by attempting to interpret the differences in accuracy. In Article 3, we obtain better predictive accuracy. The subsection therefore refers to the actions and events which triggered the case and gave rise to a claim made by an individual to the effect that the ECHR was violated by some state.

That happens in cases that the Court deems inadmissible, concluding to a judgment of non-violation. The combination also yields slightly better performance for Articles 6 and 8 while performance marginally drops for Article 3.

That is. Without going into details with respect to a particularly complicated debate that is out of the scope of this paper, we may here simplify by observing that since the beginning of the 20th century, there has been a major contention between two opposing ways of making sense of judicial decision-making: legal formalism and legal realism Posner, ; Tamanaha, ; Leiter, Extensive empirical research on the decision-making processes of various supreme and international courts, and especially the US Supreme Court, has indicated rather consistently that pure legal models, especially deductive ones, are false as an empirical matter when it comes to cases decided by courts further up the hierarchy.

As a result, it is suggested that the best way to explain past decisions of such courts and to predict future ones is by placing emphasis on other kinds of empirical variables that affect judges Baum, ; Schauer, For example, early legal realists had attempted to classify cases in terms of regularities that can help predict outcomes, in a way that did not reflect standard legal doctrine Llewellyn, This could help explain why judges primarily react to the facts of the case, rather than to legal arguments.

Still, our work coheres well with a bulk of other empirical approaches in the legal realist vein. The topics further exemplify this line of interpretation and provide proof of the usefulness of the NLP approach. Changer la langue cible pour obtenir des traductions.

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DETENTION ET ACTE INHUMAIN OU DEGRADANT : ARTICLE 3 DE LA CEDH

Cependant, l’article 3 ne prévoit pas de restrictions, ce en quoi il contraste avec la majorité des clauses normatives de la Convention et, conformément à l’article 15 § 2, il ne souffre nulle dérogation, même en cas de danger public menaçant la vie de la nation (Labita c. Italie [GC], n o 26772/95, § 119, CEDH 2000‑IV, Selmouni ...

For 6 and 8, we use the 3 data for parameter tuning, while for 3 we use 8. and Discussion Predictive accuracy. We compute the predictive performance of both sets of features on the classification of the ECtHR cases. Performance is computed as . 3 Cedh Dissertation I had no time to compete my dissertation, but my friend this website. The second paper I ordered was a research report on history. I . 20/04/ · Analyse de 3 de la Convention Européenne des Droits de l'Homme (CEDH) Dans cette même convention, 3 dispose que nul ne sera soumit à la torture, ni a des peines ou traitements cruels, inhumains ou dégradants Cet pose trois concepts: les traitements inhumains, les traitements dégradants et enfin la torture.

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