Film har en stor efterfrågan och en sak som kan avgöra vilken film en person väljer att tittapå, är filmtrailern. Syftet med den här studien är att via multimodalitet analysera fyra olikafilmtrailers: Män som hatar kvinnor (2009) (svensk och amerikansk trailerversion), Get Out(2016) och Tenet (2019). Studien undersöker hur ljudelementen används inom utvaldafilmtrailers, för att se hur ljud och bild samverkar för att skapa ett intresse till att vilja se detillhörande långfilmerna. Analys visar att rytmisk bakgrundsmusik, ljudeffekter och dialogverkar ha stor betydelse i filmtrailern för Get Out (2016) och Tenet (2019), medan det somsticker ut för båda trailerversioner av Män som hatar kvinnor (2009) är avsaknaden av dialog.Studiens resultat visar att det inom skräckfilm verkar som att ljudeffekter har mer betydelseän genren kriminalfilm, men att det i actionfilm framför allt är rytmisk bakgrundsmusik somverkar utgöra det mest unika.

In political campaigning substantial resources are spent on voter mobilization, that is, on identifying and influencing as many people as possible to vote. Campaigns use statistical tools for deciding whom to target ("microtargeting"). In this paper we describe a nonpartisan campaign that aims at increasing overall turnout using the example of the 2004 US presidential election. Based on a real data set of 19,634 eligible voters from Ohio, we introduce a modern statistical framework well suited for carrying out the main tasks of voter targeting in a single sweep: predicting an individual's turnout (or support) likelihood for a particular cause, party or candidate as well as data-driven voter segmentation. Our framework, which we refer to as LORET (for LOgistic REgression Trees), contains standard methods such as logistic regression and classification trees as special cases and allows for a synthesis of both techniques. For our case study, we explore various LORET models with different regressors in the logistic model components and different partitioning variables in the tree components; we analyze them in terms of their predictive accuracy and compare the effect of using the full set of available variables against using only a limited amount of information. We find that augmenting a standard set of variables (such as age and voting history) with additional predictor variables (such as the household composition in terms of party affiliation) clearly improves predictive accuracy. We also find that LORET models based on tree induction beat the unpartitioned models. Furthermore, we illustrate how voter segmentation arises from our framework and discuss the resulting profiles from a targeting point of view. (authors' abstract)

This study examines the theme of hybridity in Charlotte Brontë’s novel Jane Eyre and Jordan Peele’s film Get Out. Both the narrative text in the novel and the script with visual elements of the film use the concept of hybridity through Gothic motifs: a mad non-white woman in the attic in Jane Eyre and a psychological place in Get Out, where members of a white family hypnotise black people in order to exploit their physical capabilities. This is employed to disrupt dominant discourses of authoritative class, revealing the ways in which these discourses are constructed through the exclusion of certain identities. Bertha Mason, the Creole wife of Edward Rochester in Jane Eyre, and Chris Washington, the African American protagonist of Get Out, both embody a sense of hybridity that challenges established norms of individuality and representation. Through a comparative analysis of these characters, this essay argues that hybridity serves as a means of exposing and subverting the power structures that reinforce presiding stereotypes of othered characters. By deconstructing these sovereign discourses, hybridity creates space for alternative voices and perspectives that are often excluded from ascendant literatures. Ultimately, this essay accentuates the importance of inspecting the intersectional identities of characters in literature and film, as a means of challenging prepotent discourses and promoting social justice.

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Political campaigning has become a multi-million dollar business. A substantial proportion of a campaign's budget is spent on voter mobilization, i.e., on identifying and influencing as many people as possible to vote. Based on data, campaigns use statistical tools to provide a basis for deciding who to target. While the data available is usually rich, campaigns have traditionally relied on a rather limited selection of information, often including only previous voting behavior and one or two demographical variables. Statistical procedures that are currently in use include logistic regression or standard classification tree methods like CHAID, but there is a growing interest in employing modern data mining approaches. Along the lines of this development, we propose a modern framework for voter targeting called LORET (for logistic regression trees) that employs trees (with possibly just a single root node) containing logistic regressions (with possibly just an intercept) in every leaf. Thus, they contain logistic regression and classification trees as special cases and allow for a synthesis of both techniques under one umbrella. We explore various flavors of LORET models that (a) compare the effect of using the full set of available variables against using only limited information and (b) investigate their varying effects either as regressors in the logistic model components or as partitioning variables in the tree components. To assess model performance and illustrate targeting, we apply LORET to a data set of 19,634 eligible voters from the 2004 US presidential election. We find that augmenting the standard set of variables (such as age and voting history) together with additional predictor variables (such as the household composition in terms of party affiliation and each individual's rank in the household) clearly improves predictive accuracy. We also find that LORET models based on tree induction outbeat the unpartitioned competitors. Additionally, LORET models using both partitioning variables and regressors in the resulting nodes can improve the efficiency of allocating campaign resources while still providing intelligible models. / Series: Research Report Series / Department of Statistics and Mathematics

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