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oai:dspace.mit.edu:1721.1/1569822024-09-25T03:28:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Jackson, Jason Raghavan, Manish Cmehil-Warn, Christian Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Institute for Data, Systems, and Society Technology and Policy Program 2024-09-24T18:24:48Z 2024-09-24T18:24:48Z 2024-05 2024-07-25T14:17:35.091Z https://hdl.handle.net/1721.1/156982 The 2023 SAG-AFTRA and WGA strikes on Hollywood were notable because of their explicit ties to technology and labor’s changing relationships. In particular, disputes around using generative AI in the workplace were widely reported in the news. This thesis examines the Hollywood strikes in two parts. The first part takes a political economy approach to examine the underlying causes of these changes in technology-labor relations. In particular, the thesis argues that an industry shift to distribution via streaming services alongside increased vertical integration brought about new imperatives to production and exponentially increased levels of data capture, enabling the labor conditions that led to the strike. Theories of creative labor and technology-labor relations are used to describe the tensions. The resulting SAG-AFTRA and WGA collective bargaining agreements are then examined within these framings. The second part of the thesis quantitatively explores the relationship between news media (which its own complex relationship with technology) and the Hollywood strikes using natural language processing techniques. Sentiment analysis and sentence embeddings are used to quantify and compare news articles across different characteristics. The results of the analysis are inconclusive. S.M. S.M. Massachusetts Institute of Technology Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ Hollywood Workers vs Tech: In Theory and In the News Thesis application/pdf Master Master of Science in Electrical Engineering and Computer Science Master of Science in Technology and Policy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a2a38175-01c6-45cc-a68f-bcac2ee4a85a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Hollywood Workers vs Tech: In Theory and In the News</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2024-05</PublicationDate> <Authors> <Author> <DisplayName>Cmehil-Warn, Christian</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://creativecommons.org/licenses/by-nc-nd/4.0/</License> <Abstract>The 2023 SAG-AFTRA and WGA strikes on Hollywood were notable because of their explicit ties to technology and labor’s changing relationships. In particular, disputes around using generative AI in the workplace were widely reported in the news. This thesis examines the Hollywood strikes in two parts. The first part takes a political economy approach to examine the underlying causes of these changes in technology-labor relations. In particular, the thesis argues that an industry shift to distribution via streaming services alongside increased vertical integration brought about new imperatives to production and exponentially increased levels of data capture, enabling the labor conditions that led to the strike. Theories of creative labor and technology-labor relations are used to describe the tensions. The resulting SAG-AFTRA and WGA collective bargaining agreements are then examined within these framings. The second part of the thesis quantitatively explores the relationship between news media (which its own complex relationship with technology) and the Hollywood strikes using natural language processing techniques. Sentiment analysis and sentence embeddings are used to quantify and compare news articles across different characteristics. The results of the analysis are inconclusive.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>