Mythemes of the North can originate from different sources, such as literary works, press reports, films, opinion surveys and so on. At the same time, media often contribute to consolidate mythemes or to discuss and dismantle them. It is relevant to assess which mythemes have resisted over time, as well as to find out relations between mythemes in an alleged consistency of the image of the North (meant as one form of the knowledge of the North). Another meaningful perspective, however, might be that of comparing different representations of the North, or even of a single nation/culture, in works published in the same period, if not the same year.
This paper has the purpose to consider specific case studies, which display both evident similarities and essential differences: as indicated in the title, all works came out in the period 1927-1929 and they all offer, more or less explicitly, an image of Denmark and its people: : Karin Michaëlis’ Bibi. En lille Piges Liv (1927, 1929), Hans Kirk’s Fiskerne (1928) and Jens August Schade’s Sjov i Danmark (1928).Moreover, all works own a peculiar place in the respective author’s literary production. On the other hand, they belong to very different genres, like children’s literature, realist novel, surrealist poem. While taking all elements (potential mythemes) of Denmark contained in these works into account and connecting and comparing them, my contribution is at least an experimental attempt to investigate the image of Denmark in three famous literary works of the same period, at most a way to explore the potentialities of an approach lead by the theory of mythemes.
The webinars will be held every third Thursday of the month from 6 p.m. to 7:30 p.m. (Central European Time) at https://unistra.adobeconnect.com/lce-mytheme. Everyone is welcome to join and to participate in the discussion. The presentations will be made available soon after on our website.
Organizers: Alessandra Ballotti (Lorraine University), Claire McKeown (Lorraine University), Thomas Mohnike (University of Strasbourg), and Pierre-Brice Stahl (Sorbonne University).
First published , rewritten and updated June 22, 2020
Trying to understand the narrative grammar of the mythèmes used in the Snorra-Edda, I analysed the 80 most frequent tokens, dismissing of course stopwords, with our mythème-laboratory. I used Rasmus Anderson’s English translation (1879), that does include all major mythological passages. I defined discs of 80 words around each search token with a sample coverage rate of less than 50%. Only those words are retained where the number of words in the work is greater than 20 and the over-representation coefficient in the sample is greater than 1.2. The radius of the discs may be automatically reduced to comply with the limit specified for the recovery rate. See the details here. I imported the data to gephi, interpreting the search token as source node and the tokens significant defining tokens as target. Weight of edges corresponds to the CSRR, that is the normalized importance of the coefficient of over-representation. In order to gain a readable layout, I ran the Force-Atlas-layout-algorithm.
A first map places the used mythèmes quite nicely in the context of other mythemes used in the same context: As in the text, Har and Gangleri are highly connected, as they are dialoging throughout the text as are the place of the hall as the location of telling stories. Loke and Thor are undertaking journeys together, accompanied of Thor’s hammer and liked both to the Aesir and the giants. God, heaven, earth and the world are put together and with these mythemes questions of cosmology. Odin is linked too questions of kingdom and fatherhood. Spots in dark green have a high degree, that is are highly connected to others, spots in light green less.
In the above map, giants seem to take an important place. To verify the centrality of the different mythèmes, I run the algorithms that define centrality of the different nodes assigned colors and font size according to the centrality of the elements in the graph. The degrees of the nodes were weighted. The next three graphs show three different resulting variants. Attention : the orientation of the map has changed as I rerun several algorithms, but the general structure remained unchanged. Dark blue nodes are central.