Agrégateur de flux

The Chinese Communist Party has stepped up efforts to spread its influence in Europe. Whether it be through students, academics, "friends of China" groups or cultural players, China's ruling party is initiating new channels of communication coordinated directly by the United Front Work Department. [...]

A new trove of leaked emails from former Israeli prime minister Ehud Barak and former defence minister Benny Gantz have exposed further details about the business activities of discreet private intelligence firms Black Cube and Psy-Group, and their relationship with [...]

With its private beach, two pools and high-end rooms, the Veranda Pointe aux Biches four-star hotel is one of the [...]

Frenchman Marcus Détrez, 27, took up a consulting role earlier this month at the China University of Petroleum (CUPB), a [...]

In an unexpected consequence of the war in Ukraine, new nationalist groups have been on the rise and their political [...]

The UK's MI5 and MI6 are being sued by four members of a Libyan family in the latest action alleging [...]

Beijing’s core aims are clear—and limited.

How the EU can funnel its wealth into its defense.

The existential catastrophe faced by the population of the Gaza Strip currently looms large in the foreign policy and security debates. The plight of civilians there is par­ticularly acute. Yet, severe crises persist elsewhere too – from Ukraine and Sudan to Myanmar, the Democratic Republic of Congo, and Haiti – where protracted violent conflicts continue to cause grave suffering among civilians. This grim reality is under­scored in the United Nations Secretary-General’s latest annual report, released in May. At the same time, conventional mechanisms for international conflict resolution are failing in an increasing number of contexts. In light of this, it is crucial to system­atically track evolving conflict dynamics and to revise approaches to the protection of civilians accordingly.

The European Union operates largely in accordance with the principles of consensus democracy – that is, it seeks to integrate as many parties spanning the political spec­trum of its member states as possible. Amid the recent growth of far-right parties at both the national and European level, this approach has led to the increased participation of such forces in EU institutions. Analysis of key actors at the EU level shows that since no later than the 2024 European elections, representatives of far-right par­ties have been involved in all major EU decisions. The centres of their influence are the European Council and the Council of the EU, where they participate as leaders or partners in national governments. But they are increasingly becoming more influential in the European Parliament, which has shifted to the right and where alternative majorities are now possible. At the same time, significant differences remain between the far-right parties. Ultimately, the extent of their influence and which far-right trend predominates within the EU system depends mainly on the largest force in European politics – the European People’s Party.

Die Europäische Union operiert in weiten Teilen nach den Prinzipien einer Konsensdemokratie, die darauf ausgerichtet ist, möglichst das komplette politische Spektrum ihrer Mitgliedstaaten zu integrieren. Angesichts der Zuwächse von Rechts­außen­parteien auf nationaler wie auf europäischer Ebene vermehrt sich daher zunehmend auch ihr Einfluss in den EU-Institutionen. Die Analyse der zentralen Akteure auf EU‑Ebene zeigt: Spätestens seit den Europawahlen 2024 sind Vertreter:innen von Rechtsaußenparteien in nahezu allen EU-Entscheidungsprozessen präsent. Die Schwer­punkte ihres Einflusses liegen – aufgrund ihrer Teilhabe an nationalen Regierungen – im Europäischen Rat und im Rat der EU, zunehmend aber auch im nach rechts gerück­ten Europäischen Parlament, in dem inzwischen alternative Mehrheitskonstellationen möglich sind. Gleichzeitig bleiben die Unterschiede innerhalb des Rechtsaußenspektrums groß. Wie prägend dessen Einfluss ist und welche Strömung sich unter den Rechtsaußenparteien durchsetzt, hängt maßgeblich von der Europäischen Volks­partei (EVP) ab.

Vu de l'étranger, les Français sont souvent perçus comme des râleurs, des fainéants, en gros des grévistes en puissance ! Notamment vis à vis de ses voisins européens. Mais si les chiffres ne montrent pas le contraire, existe-t-il réellement une "culture de la grève" à la française ? Les explications de Cécile Galluccio

China’s limited success in managing the political risks of the BRI has resulted in reputational costs and played a role in China’s curtailed flow of BRI lending in recent years.

Far from consolidating, recognition by the international community could expose the group’s rifts over resources, external demands, and identity.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.

Les lycéens et étudiants sont venus gonfler les rangs des syndicats de travailleurs, jeudi, lors de la manifestation parisienne contre des mesures budgétaires jugées "brutales" par les organisations syndicales. Reportage.

Written by Gisela Grieger.

The importance of the EU’s trade defence arsenal is underscored, among other factors, by persistent global overcapacity in a range of sectors, which has significant distorting effects on international markets, and by the weaponisation of trade, including through economic coercion amid growing geopolitical tensions.

The arsenal can be divided into two categories. First, the EU’s traditional trade defensive instruments (TDIs), which are based on multilateral trade agreements going back to Codes developed under the 1947 General Agreement on Tariffs and Trade; and second, the EU’s more recent autonomous trade instruments, most of which were enacted between 2019 and 2024.

TDIs enable the EU to deter and combat unfair trade practices from companies and public authorities of third countries, shield EU industries and jobs from these practices, and restore a level playing field for EU companies in the internal market. TDIs are mainly applied in the form of additional duties on imports of dumped and/or subsidised goods, or on goods whose imports have surged suddenly and unexpectedly and have caused serious injury to EU industry – or threaten to do so.

The EU’s autonomous trade instruments seek to fill regulatory gaps in international trade law in areas such as public procurement and foreign subsidies, with a view to levelling the playing field between EU companies and non-EU companies and to safeguarding the EU’s economic interests, including its economic security.

Against the backdrop of the United States’ recent unilateral tariff policies, which are likely to lead to a diversion of trade flows to other markets, including the EU, and to a further increase in the global use of trade defence measures, the relevance of the EU’s trade defence toolbox is set to grow in the future.

Read the complete briefing on ‘Understanding the EU trade defence toolbox‘ in the Think Tank pages of the European Parliament.

La campagne électorale prend fin ce jeudi. Dimanche, les Guinéens sont appelés à voter la nouvelle Constitution.