Difference between revisions of "Cultural Spheres Network"
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|valuelabels = No. of chared cultural characteristics | |valuelabels = No. of chared cultural characteristics | ||
|techname = cult_spheres | |techname = cult_spheres | ||
| − | |category= [[ | + | |category= [[Communication|Culture]] |
|label = Cultural Spheres Network | |label = Cultural Spheres Network | ||
| − | |relatedindicators = | + | |relatedindicators = [[Culture|Culture]] |
|description = With our data set on ‘cultural spheres’ we introduce an innovative way of describing these configurations in a relational way. Given that global cultural clusters of countries do not necessarily have rigid, clear-cut boundaries or ‘fault lines’, we apply valued two-mode social network analysis to define cultural similarities. Following this approach, countries can be tied by sharing a multitude of cultural characteristics. We draw on a variety of variables that describe cultural characteristics like dominant religion(s), dominant language, colonial history, gender relations, civil freedom etc. As a result, we get a fuzzy typology of cultural spheres. This typology consists of yearly valued networks, spanning a time frame of 1789 until 2010. The more of these characteristics two countries share, the more closely connected they are. We assume that configurations of statehood and state policies not only correspond to world regions, but also to cultural spheres that can be characterized empirically by consolidated relations in dynamic subnetworks. | |description = With our data set on ‘cultural spheres’ we introduce an innovative way of describing these configurations in a relational way. Given that global cultural clusters of countries do not necessarily have rigid, clear-cut boundaries or ‘fault lines’, we apply valued two-mode social network analysis to define cultural similarities. Following this approach, countries can be tied by sharing a multitude of cultural characteristics. We draw on a variety of variables that describe cultural characteristics like dominant religion(s), dominant language, colonial history, gender relations, civil freedom etc. As a result, we get a fuzzy typology of cultural spheres. This typology consists of yearly valued networks, spanning a time frame of 1789 until 2010. The more of these characteristics two countries share, the more closely connected they are. We assume that configurations of statehood and state policies not only correspond to world regions, but also to cultural spheres that can be characterized empirically by consolidated relations in dynamic subnetworks. | ||
|codingrules = Several binary variables have been used for the construction of this network (they can be found under the subcategory "Cultural Characteristics". We specifically coded them as dichotomous variables i.e. they either have the value 1 or 0. In all cases, 1 depicts some characteristic like Islam being the dominant religion or being colonized by Germany. A value of 0 would mean that this country does not have that characteristic. We end up with a matrix for every single observed year respectively. These matrices show the countries in the rows and the cultural characteristics in the column coded, as described above, as 0 or 1. This is essentially a two-mode network depicting the membership of countries in a number of different cultural categories. From these bipartite networks we projected the one-mode networks with the assumption that if two states share the same characteristic, they have one tie with one another. While doing so, we compute a weighted network by simply adding up the number of similar connections. | |codingrules = Several binary variables have been used for the construction of this network (they can be found under the subcategory "Cultural Characteristics". We specifically coded them as dichotomous variables i.e. they either have the value 1 or 0. In all cases, 1 depicts some characteristic like Islam being the dominant religion or being colonized by Germany. A value of 0 would mean that this country does not have that characteristic. We end up with a matrix for every single observed year respectively. These matrices show the countries in the rows and the cultural characteristics in the column coded, as described above, as 0 or 1. This is essentially a two-mode network depicting the membership of countries in a number of different cultural categories. From these bipartite networks we projected the one-mode networks with the assumption that if two states share the same characteristic, they have one tie with one another. While doing so, we compute a weighted network by simply adding up the number of similar connections. | ||
Revision as of 14:54, 3 May 2021
| Quick info | |
|---|---|
| Data type | Numeric |
| Scale | Metric |
| Value labels | No. of chared cultural characteristics |
| Technical name | cult_spheres |
| Category | Culture |
| Label | Cultural Spheres Network |
| Related indicators | Culture |
With our data set on ‘cultural spheres’ we introduce an innovative way of describing these configurations in a relational way. Given that global cultural clusters of countries do not necessarily have rigid, clear-cut boundaries or ‘fault lines’, we apply valued two-mode social network analysis to define cultural similarities. Following this approach, countries can be tied by sharing a multitude of cultural characteristics. We draw on a variety of variables that describe cultural characteristics like dominant religion(s), dominant language, colonial history, gender relations, civil freedom etc. As a result, we get a fuzzy typology of cultural spheres. This typology consists of yearly valued networks, spanning a time frame of 1789 until 2010. The more of these characteristics two countries share, the more closely connected they are. We assume that configurations of statehood and state policies not only correspond to world regions, but also to cultural spheres that can be characterized empirically by consolidated relations in dynamic subnetworks.
Coding rules
Several binary variables have been used for the construction of this network (they can be found under the subcategory "Cultural Characteristics". We specifically coded them as dichotomous variables i.e. they either have the value 1 or 0. In all cases, 1 depicts some characteristic like Islam being the dominant religion or being colonized by Germany. A value of 0 would mean that this country does not have that characteristic. We end up with a matrix for every single observed year respectively. These matrices show the countries in the rows and the cultural characteristics in the column coded, as described above, as 0 or 1. This is essentially a two-mode network depicting the membership of countries in a number of different cultural categories. From these bipartite networks we projected the one-mode networks with the assumption that if two states share the same characteristic, they have one tie with one another. While doing so, we compute a weighted network by simply adding up the number of similar connections.
For further information see the Technical Paper: Besche-Truthe, Fabian; Seitzer, Helen; Windzio, Michael. 2020 “Cultural Spheres – Creating a dyadic dataset of cultural proximity”. SFB 1342 Technical Paper Series, 5. Bremen, SFB 1342.
Bibliographic info
Citation: Technical Paper
Related publications: NA (no information available)
Misc
Project manager(s): Besche-Truthe, Fabian
Data release: 2020.11.25
Revisions: NA (no information available)
Sources
NA (no information available)
