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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorMeijers, Evert
dc.contributor.authorLand, Ruben
dc.date.accessioned2024-08-07T23:05:46Z
dc.date.available2024-08-07T23:05:46Z
dc.date.issued2024
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/47136
dc.description.abstractThe Netherlands is sometimes perceived as polarized between the urban center named the Randstad including the four main cities Amsterdam, Den Haag, Rotterdam and Utrecht, and the rest of the country, the regio. The gas extraction-induced earthquakes in Groningen for example pit the center against this province. People in Groningen feel that their province is exploited to fill the state coffers, while their concerns are not being taken seriously by the politicians in Den Haag. Survey research indeed finds a divide between the center and periphery, as the regio finds itself relatively neglected by the national politics. To date, no research has been conducted on the existence of this divide in the political debates themselves. This thesis aims therefore to map the extent to which the contexts of places in the Randstad and regio differ in political debates. Different contexts could hint at disparate treatment. This is done by using the word embedding model word2vec for mapping toponyms in vector space. Larger distances in vector space indicate more divergent contexts. Distances in vector space are then modelled in a gravity model. Results show that the division between Randstad and regio only effects the context around places in a minor way, indicating the nuance there is in the debates. Individual place characteristics, for example, are of a greater importance.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectRandstand and regio in the Dutch Parliament: A phantom gap? (preliminary)
dc.titleRandstand and regio in the Dutch Parliament: A phantom gap? (preliminary)
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsnetwork analysis; politics; word embeddings; word2vec; The Netherlands; toponyms
dc.subject.courseuuApplied Data Science
dc.thesis.id36224


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