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Conspiracy Beliefs about Jews and Muslims in Norway
Author(s): Dyrendal, Asbjørn
Editor(s): Hoffmann, Christhard; Moe, Vibeke
Date: 2020
Topics: Main Topic: Antisemitism, Antisemitism, Surveys, Islamophobia, Antisemitism: Attitude Surveys
Abstract: Studies of conspiracy beliefs in Scandinavian countries have been few and qualitative in nature. This chapter analyses recent surveys and gives tentative answers as to how international research findings about conspiracy beliefs hold up in a Norwegian setting. Some of the expected effects were found. Two surveys validate the five-item conspiracy mentality scale for Norway, a measure of the generalised propensity towards believing in conspiracy theories. Scores on conspiracy mentality predicted belief in single-item conspiracy beliefs regarding Jews and Muslims, but the effect size was small. Conspiracy stereotypes of Jews and Muslims were a contributing factor in a more general xenophobia and correlated positively with measures of social distance. The conspiracy stereotypes contributed to explaining differences in views on the legitimacy of violence towards members of outgroups in general. Contrary to expectations, anti-Muslim conspiracy beliefs were more closely tied to conspiracy mentality than antisemitic ones. With regard to the debate on whether adherents of the political far left and far right believe in conspiracy theories more than those of centrist and mainstream parties, the Norwegian left-wing adherents generally scored lower on conspiracy beliefs about Jews and Muslims. Conspiracy theories were for election winners: the populist right generally scored significantly higher than other political orientations. The differences in scores were particularly strong for anti-Muslim beliefs. The analyses were run by adopting questions asked for other purposes. With the exception of conspiracy mentality, scales were constructed by using those survey items that were arguably approximate items to those in reliable measures. Further inquiries should adapt established scales for more robust answers and in order to build reliable models.