FROM NEW YORK CITY TO PARIS. CRIME SERIES ADAPTATIONS: A MULTIMODAL DIGITAL SEMIOTICS PROCEDURE.
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Date
2020-01-20
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Department
Language, Literacy & Culture
Program
Language Literacy and Culture
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Distribution Rights granted to UMBC by the author.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Abstract
This dissertations examines adaptations of TV series in multiple countries by focusing on transnational TV series adaptations as an ideal platform to study the complexities of cultural representations and productions in the context of globalization. To conduct my research, I use the popular American TV crime show: Law & Order: Criminal Intent and its French adaptation Paris Enque?tes Criminelles. My research project uses the methods of multimodality, film studies, and intercultural studies, and a new technological approach to analyze transnational TV shows. The goal is to automatically extract and manage big data to uncover trends of cultural representation on screen. To do so, I propose a method, called the Multimodal Intercultural Matrix (MIM) model, that enables reverse engineering a show and to quantify the various elements of the episodes for cultural analysis. The MIM model dissects the show into three cultural categories, named Power, Language, and Society. Each of those categories is constructed by, and intersects with, three different modes: Cinematography, Non-Verbal Communication, and Speech. Following this method, and based on concepts of close and distant reading, this dissertations provides an in-depth scene analysis both demonstrating the proposed methodology and suggesting how it can be used on a larger scale for other projects. In collaboration with an artificial intelligence engineer, Anjal Amin, who developed an AI software system called the Mo?bius Trip, the software allowed me to mine big data sets from the television programs studied. It also allows for the automatic and systematic analysis of the show's episodes. The software, for example, recognizes the characters' genders and facial expressions. With the Mo?bius Trip, the dissertations objectively demonstrates that women have significantly less onscreen time in the US than they have in France. It also shows that men are more likely to display anger while women exhibit fear in both cultures. France tends to be more egalitarian in terms of gender roles than in the US. Nonetheless, men seem to remain the oppressors and women the victims in both versions.