Suchergebnisse

Motive ausländischer Studenten für das Studium in Deutschland und
Bewertung der Aufnahme in der Bundesrepublik.

Themen: Unterschiede im Deutschlandbild vor der Einreise und derzeitig;
Art und Quellen der Informationen über die BRD; Aufenthaltsdauer;
Berufswunsch; Chancengleichheit der Berufswahl bei Studium im
Heimatland; Intensität des Kontakts zu Studienkollegen und
Universitätspersonal; Freizeitgestaltung; Briefkontakte in die Heimat;
Schwierigkeiten bei der Wohnungssuche; Kontakte zur deutschen
Bevölkerung; erfahrene Diskriminierung; Einstellung zur deutschen
Bevölkerung (semantisches Differential); vermutetes Heterostereotyp;
Umorientierung der Ansprüche; politisches, kulturelles und
wirtschaftliches Wissen über die BRD; politische Einstellung;
finanzielle Situation; Sprachkenntnisse; Mediennutzung; soziale
Herkunft; Zeitbudget.

Demographie: Alter (klassiert); Staatsangehörigkeit; Familienstand;
Kinderzahl; Alter und Anzahl der Geschwister; Konfession; Religiosität,
regionale Herkunft.

Interviewerrating: Kooperationsbereitschaft des Befragten; Anwesenheit
anderer Personen; Sprachbeherrschung; äußerer Eindruck.

This database compiles secondary data originating from the National Statistical Offices of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan.

The database includes information about major agricultural statistics such as structure of the agricultural sector, land use, production of most important crops and livestock outputs as well as size of population at the level of provinces (oblasts). Most land use and production data are available for the three farm types, agricultural enterprises, individual farms, and households, and in aggregated form. Time coverage ranges from 1992 to 2014 (with few statistics additionally for 1991 and 2015), with some gaps in early years after independence.

The database is a product of the research project "AGRIWANET – Agricultural Restructuring, Water Scarcity and the Adaptation to Climate Change in Central Asia: A Five-Country Study".

The car brand's market share indicates the relative market share of a brand compared to others within a geographic area. The dataset comprises information on 14 brands: Audi, BMW (including Alfa Romea and Lanica), Ford, Mazda, Mercedes, Nissan, Opel, Peugeot (including Citroёn), Renault, other Asian car brands, other brands, Toyota (including Lexus), VW. All car indicators are based on the central car register of the Federal Motor Transport Authority (Kraftfahrt-Bundesamt, KBA) in Flensburg (microm 2016, p. 94).

The car brand's market share indicates the relative market share of a brand compared to others within a geographic area. The dataset comprises information on 14 brands: Audi, BMW (including Alfa Romea and Lanica), Ford, Mazda, Mercedes, Nissan, Opel, Peugeot (including Citroёn), Renault, other Asian car brands, other brands, Toyota (including Lexus), VW. All car indicators are based on the central car register of the Federal Motor Transport Authority (Kraftfahrt-Bundesamt, KBA) in Flensburg (microm 2016, p. 94).

The car brand's market share indicates the relative market share of a brand compared to others within a geographic area. The dataset comprises information on 14 brands: Audi, BMW (including Alfa Romea and Lanica), Ford, Mazda, Mercedes, Nissan, Opel, Peugeot (including Citroёn), Renault, other Asian car brands, other brands, Toyota (including Lexus), VW. All car indicators are based on the central car register of the Federal Motor Transport Authority (Kraftfahrt-Bundesamt, KBA) in Flensburg (microm 2016, p. 94).

In an era of increasing political challenges to global and regional organizations, it is crucial to understand how they claim legitimacy and how successful they are in this respect. Yet, we know surprisingly little about the sources and consequences of legitimation in most regional organizations worldwide. In this project, we thus map the legitimation strategies of a sample of 28 organizations between 1980 and 2019.

Regional organizations in the sample:
Africa
- Organization of African Unity/African Union (OAU/AU)
- Central African Economic and Monetary Union (CEMAC)
- Common Market for Eastern and Southern Africa (COMESA)
- East African Community (EAC)
- Economic Community of West African States (ECOWAS)
- Inter-Governmental Authority on Development (IGAD)
- Southern African Customs Union (SACU)
- Southern African Development Community (SADC)
Asia-Pacific
- Association of Southeast Asian Nations (ASEAN)
- Gulf Cooperation Council (GCC)
- Pacific Island Forum (PIF)
- South Asia Association for Regional Cooperation (SAARC)
- Shanghai Cooperation Organization (SCO)
Americas
- Andean Pact/Andean Community (CAN)
- Caribbean Community (CARICOM)
- Common Market of the South (Mercosur)
- Organization of American States (OAS)
- Organization of Eastern Caribbean States (OECS)
- Central American Integration System (SICA)
Europe
- Council of Europe (COE)
- European Free Trade Association (EFTA)
- European Union (EU)
- Nordic Council (NordC)
Cross-Regional
- Asia-Pacific Economic Cooperation (APEC)
- Commonwealth of Independent States (CIS)
- League of Arab States (LoAS)
- Organization of Arab Petroleum Exporting Countries (OAPEC)
- Organization for Security and Co-operation in Europe (OSCE)

Online messaging app Telegram has increased in popularity in recent years surpassing Twitter and Snapchat by the number of active monthly users in late 2020. The messenger has also been crucial to protest movements in several countries in 2019-2020, including Belarus, Russia and Hong Kong. Yet, to date only few studies examined online activities on Telegram and none have analyzed the platform with regard to the protest mobilization. In the present study, we address the existing gap by examining Telegram-based activities related to the 2019 protests in Hong Kong. With this paper we aim to provide an example of methodological tools that can be used to study protest mobilization and coordination on Telegram. We also contribute to the research on computational text analysis in Cantonese - one of the low-resource Asian languages, - as well as to the scholarship on Hong Kong protests and research on social media-based protest mobilization in general. For that, we rely on the data collected through Telegram's API and a combination of network analysis and computational text analysis. We find that the Telegram-based network was cohesive ensuring efficient spread of protest-related information. Content spread through Telegram predominantly concerned discussions of future actions and protest-related on-site information (i.e., police presence in certain areas). We find that the Telegram network was dominated by different actors each month of the observation suggesting the absence of one single leader. Further, traditional protest leaders - those prominent during the 2014 Umbrella Movement, - such as media and civic organisations were less prominent in the network than local communities. Finally, we observe a cooldown in the level of Telegram activity after the enactment of the harsh National Security Law in July 2020. Further investigation is necessary to assess the persistence of this effect in a long-term perspective.