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Paper #1: published 

Transnational TV Series Adaptations: What Artificial Intelligence Can Tell Us About Gender Inequality In France And The US

ABSTRACT

The present research analyzes the inequality of gender representation in transnational TV series. For this purpose, a content analysis was carried out on 18 episodes of the US crime show Law & Order: Criminal Intent and its French adaptation Paris Enquêtes Criminelles. To conduct this research, we used the artificial intelligence toolkit the Möbius Trip, which is equipped with a gender and emotion recognition feature and relies on big data. The main findings indicate that male characters overwhelmingly dominate the onscreen time equally in both the US and the French versions. The data also show that male characters are more emotionally expressive and that women tend to display a wider range of emotions. The French characters are slightly more emotionally expressive than their American counterparts. The data also suggest that male characters tend to display violent behavior and that female characters tend to be portrayed as a victim in both versions of the show. The emotions-related results show a trend, but the difference of emotions between male and female characters and between the French and American cultures remain fairly narrow.

Gender Inequality

Paper available here: 

 

 

Zooming in on Shot Scales:
A Digital Approach to Reframing Transnational TV series Adaptations

 

Abstract

This pilot study illustrates an empirical cross-cultural comparative analysis of transnational TV series adaptations. It investigates patterns of shot scale distribution in conjunction with gender and display of emotions to uncover and compare cultural representation in France and the US. The study showcases 16 episodes of Law & Order: Criminal Intent and its French adaptation, Paris Enquêtes Criminelles, for a total of 44,602 frames. Relying on the deep learning toolkit, the Möbius Trip, we propose an objective shot-scale model to frame and quantify a large quantity of data. We propose a layered, four-level reading of the data in the light of intercultural models, media theories, and feminist and psychological approaches to articulate cultural decoding. This study provides insights into the ethics of televisual representations of male and female characters on screen across cultures and the process through which cultural proximity is achieved.

2021 IEEE International Conference on Industrial Engineering and Engineering Management

Born from a collaboration between a humanities scholar and an artificial intelligence engineer, the Möbius Trip is a deep-learning multimodal toolkit that can predict TV series genre based on the combination of color, light, rhythm, and music. Following the tradition of formalist film theorists concerned with the technical elements of motion pictures, and Cultural Analytics, which focuses on computational, visualization, and big-data methods, we seek patterns in the film-making cultural tradition between the US and France. Our results indicate that Transnational TV series Adaptations, the transfer of a show into another culture, also implies a transfer in genre. Our goal is to offer a potent toolkit that can help TV series adaptors make more intentional decisions when adapting a show and make the adapted show more successful.

2020 IEEE International Conference on Industrial Engineering and Engineering Management

Landry Digeon Ph.D. and Anjal Amin developed a method and an image analysis software named "the Möbius Trip." This software runs on deep learning and allows us to gather and manage big data. Our goal is to reverse-engineer TV series episodes and measure cultural and cinematographic elements. We found that there are significantly more male characters in both Law & Order and in Paris Enquêtes Criminelles. Men are portrayed as violent while women are more likely to be portrayed as victims.

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