Difference between revisions of "Georgian Language Group"
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| − | |techname = | + | |techname = cult_lang2_bin11 |
|category= [[Culture|Culture]] | |category= [[Culture|Culture]] | ||
| − | |label = | + | |label = Georgian Language Group |
| − | |relatedindicators = | + | |relatedindicators = <ul> |
| − | |description = The language spoken belongs to the | + | <li>[[cult_lang_arabic| Arabic as predominant language ]] |
| + | <li>[[cult_lang_english| English as predominant language ]] | ||
| + | <li>[[cult_lang_spanish| Spanish as predominant language ]] | ||
| + | <li>[[cult_lang2_bin1 | Albanian Language Group ]] | ||
| + | <li>[[cult_lang2_bin10 | French based Language Group ]] | ||
| + | <li>[[cult_lang2_bin12 | Germanic Language Group ]] | ||
| + | <li>[[cult_lang2_bin13 | Greek Language Group ]] | ||
| + | <li>[[cult_lang2_bin14 | Indo-Iranian Language Group ]] | ||
| + | <li>[[cult_lang2_bin15 | Italic Language Group ]] | ||
| + | <li>[[cult_lang2_bin16 | Japonic Language Group ]] | ||
| + | <li>[[cult_lang2_bin17 | Kam-Tai Language Group ]] | ||
| + | <li>[[cult_lang2_bin18 | Kongo-based Language Group ]] | ||
| + | <li>[[cult_lang2_bin19 | Koreanic Language Group ]] | ||
| + | <li>[[cult_lang2_bin2 | Armenian Language Group ]] | ||
| + | <li>[[cult_lang2_bin20 | Malayo-Polynesian Language Group ]] | ||
| + | <li>[[cult_lang2_bin21 | Mande Language Group ]] | ||
| + | <li>[[cult_lang2_bin22 | Mon-Khmer Language Group ]] | ||
| + | <li>[[cult_lang2_bin23 | Mongolic Eastern Language Group ]] | ||
| + | <li>[[cult_lang2_bin24 | Ngbandi based Language Group ]] | ||
| + | <li>[[cult_lang2_bin25 | Semitic Language Group ]] | ||
| + | <li>[[cult_lang2_bin26 | Tibeto-Burman Language Group ]] | ||
| + | <li>[[cult_lang2_bin27 | Tupi-Guarani Language Group ]] | ||
| + | <li>[[cult_lang2_bin28 | Turkic Eastern Language Group ]] | ||
| + | <li>[[cult_lang2_bin29 | Turkic Southern Language Group ]] | ||
| + | <li>[[cult_lang2_bin3 | Atlantic-Congo Language Group ]] | ||
| + | <li>[[cult_lang2_bin30 | Turkic Western Language Group ]] | ||
| + | <li>[[cult_lang2_bin31 | Uralic Language Group ]] | ||
| + | <li>[[cult_lang2_bin4 | Balto-Slavic Language Group ]] | ||
| + | <li>[[cult_lang2_bin5 | Chadic Language Group ]] | ||
| + | <li>[[cult_lang2_bin6 | Chinese Language Group ]] | ||
| + | <li>[[cult_lang2_bin7 | Cushitic Language Group ]] | ||
| + | <li>[[cult_lang2_bin8 | English based Language Group ]] | ||
| + | <li>[[cult_lang2_bin9 | Finnic Language Group ]] <ul> | ||
| + | |||
| + | |description = The language spoken belongs to the Georgian language group | ||
|codingrules = To classify a country’s dominant language we used the Ethnologue database as done by Windzio (2018). We used the Level II classification which has 33 distinct categories of languages, however the linguistic similarity within those categories is very high, compared to differences of every single language spoken in a state. “To give an example: the dominant language in Brazil is Portuguese, which is an Indo-European language (level I) and belongs together with 45 other languages to the “Italic” sub-branch at level II.” (Windzio 2018, 24). We therefore have – after coding – membership of countries in 31 different families of language. To strengthen the computational weight of having a, what we call hegemonic language as the dominant one, we furthermore coded whether the dominant language in a country is English, Spanish, or Arabic. By introducing the hegemonic languages we give greater regard to specifically hegemonic languages that might be carrying cultural orientation on to a specific hegemonic cultural model. We assume the dominant language to change very slowly. Thus it is in our final dataset not a time-variant variable but a stable one over all observed years. | |codingrules = To classify a country’s dominant language we used the Ethnologue database as done by Windzio (2018). We used the Level II classification which has 33 distinct categories of languages, however the linguistic similarity within those categories is very high, compared to differences of every single language spoken in a state. “To give an example: the dominant language in Brazil is Portuguese, which is an Indo-European language (level I) and belongs together with 45 other languages to the “Italic” sub-branch at level II.” (Windzio 2018, 24). We therefore have – after coding – membership of countries in 31 different families of language. To strengthen the computational weight of having a, what we call hegemonic language as the dominant one, we furthermore coded whether the dominant language in a country is English, Spanish, or Arabic. By introducing the hegemonic languages we give greater regard to specifically hegemonic languages that might be carrying cultural orientation on to a specific hegemonic cultural model. We assume the dominant language to change very slowly. Thus it is in our final dataset not a time-variant variable but a stable one over all observed years. | ||
Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania). We are aware that these are cases that specifically depict a secession of an ethnically very dense and different entity out of a bigger one. Therefore filling missing data like this is arguably not an apt way of dealing with it. It is however, the best option for a first approach of dealing with this particular problem. | Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania). We are aware that these are cases that specifically depict a secession of an ethnically very dense and different entity out of a bigger one. Therefore filling missing data like this is arguably not an apt way of dealing with it. It is however, the best option for a first approach of dealing with this particular problem. | ||
| − | For further information see the Technical Paper: | + | For further information see the Technical Paper: [https://www.socialpolicydynamics.de/sfb-1342-publikationen/sfb-1342-technical-paper-series?publ=10072 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.] |
|citation = | |citation = | ||
|relatedpublications = NA (no information available) | |relatedpublications = NA (no information available) | ||
| − | |projectmanagers = Besche-Truthe, | + | |
| − | |datarelease = | + | |projectmanagers = Fabian Besche-Truthe, Michael Windzio, Helen Seitzer |
| − | |revisions = | + | |datarelease = <ul><li>Version 0.001: Initial release</li></ul> |
| − | |sources = https://www.ethnologue.com/ | + | |revisions = No revisions yet |
| + | |sources = https://www.ethnologue.com/ Ethnologue database | ||
}} | }} | ||
Latest revision as of 10:06, 1 November 2021
The language spoken belongs to the Georgian language group
Coding rules
To classify a country’s dominant language we used the Ethnologue database as done by Windzio (2018). We used the Level II classification which has 33 distinct categories of languages, however the linguistic similarity within those categories is very high, compared to differences of every single language spoken in a state. “To give an example: the dominant language in Brazil is Portuguese, which is an Indo-European language (level I) and belongs together with 45 other languages to the “Italic” sub-branch at level II.” (Windzio 2018, 24). We therefore have – after coding – membership of countries in 31 different families of language. To strengthen the computational weight of having a, what we call hegemonic language as the dominant one, we furthermore coded whether the dominant language in a country is English, Spanish, or Arabic. By introducing the hegemonic languages we give greater regard to specifically hegemonic languages that might be carrying cultural orientation on to a specific hegemonic cultural model. We assume the dominant language to change very slowly. Thus it is in our final dataset not a time-variant variable but a stable one over all observed years. Since there was no data available for some smaller states, that emerged very late, we filled the missing data for the following entities with the values of the entities in the parentheses: Macedonia (Greece), South-Sudan (Sudan), and Kosovo (Albania). We are aware that these are cases that specifically depict a secession of an ethnically very dense and different entity out of a bigger one. Therefore filling missing data like this is arguably not an apt way of dealing with it. It is however, the best option for a first approach of dealing with this particular problem.
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:
Related publications: NA (no information available)
Misc
Project manager(s): Fabian Besche-Truthe, Michael Windzio, Helen Seitzer
- Version 0.001: Initial release
Revisions: No revisions yet
Sources
https://www.ethnologue.com/ Ethnologue database
