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Titlebook: Immigration and Citizenship in an Enlarged European Union; The Political Dynami Simon McMahon Book 2015 Palgrave Macmillan, a division of M

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樓主: tricuspid-valve
21#
發(fā)表于 2025-3-25 06:52:25 | 只看該作者
The Local Politics of Immigration in Rome and Madrid,es and trade unions make a claim to representing the interests of the immigrant population. Their success depends on their ability to respond to the different structural contexts found in the cities studied. In doing so, it also illustrates how a common national origin does not necessarily result in
22#
發(fā)表于 2025-3-25 07:56:15 | 只看該作者
Intra-EU Migrant Politics in Italy and Spain, the meaning of ‘who is who’ in Italy and Spain, and what this means for the citizenship of new Europeans such as the Romanians in an enlarging EU. The objective of the current chapter is thus, to paraphrase Faist, to explain whether ‘old’ national and local terms of citizenship, belonging and membe
23#
發(fā)表于 2025-3-25 12:44:40 | 只看該作者
The Politics of the Roma in Italy and Spain, estimated that this represents only a very small minority of the total (Cova, 2009). This calls for a closer examination of the politics of Roma migration in these countries, which is the topic of this chapter.
24#
發(fā)表于 2025-3-25 19:46:09 | 只看該作者
Immigration and Citizenship in an Enlarged European UnionThe Political Dynami
25#
發(fā)表于 2025-3-25 21:55:41 | 只看該作者
26#
發(fā)表于 2025-3-26 01:19:05 | 只看該作者
27#
發(fā)表于 2025-3-26 07:57:32 | 只看該作者
Shozo Aokictive functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic na
28#
發(fā)表于 2025-3-26 08:50:31 | 只看該作者
29#
發(fā)表于 2025-3-26 15:55:50 | 只看該作者
G. W. Stout,N. P. O. Greenrs and suppliers to understand the competitive market. This paper proposes a new aspect-based online reviews mining method, which combines both textual data and numerical data. Firstly, the probability distribution of topics and words is constructed by LDA topic model. With word cloud images, the ke
30#
發(fā)表于 2025-3-26 17:13:14 | 只看該作者
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