Unraveling How Corporate Sustainability Narratives Enable The Democratization Of Modern Slavery

Format: On Campus
Number of Interns (Quota): 2
Duration of the Internship: 4 - 8 Weeks
Start Date: 1 July 2025
End Date: 31 July 2025
Application Deadline: 31 May 2025
Project Supervisor: Asst. Prof. Dr. Nilüfer Yapıcı

Project Description: "Battery-powered Electric Vehicles (BEVs) are hailed worldwide thanks to their sustainability claims despite their production involving severe environmental and social costs. BEVs and events surrounding them are selectively perceived, interpreted, and signified, forming a story imbued with values, viewpoints, and commonsense assumptions. In our project we seek to understand initially a) why the auto industry’s BEV perspective regarding BEV and sustainability is preferred over the other perspectives and b) how the consensus surrounding this perspective is built.

We build on theories of hegemony (Gramsci, 1971; Nyberg, Spicer, & Wright, 2013; Levy, Reinecke & Manning, 2016) and Hall’s (2019) theorization on communication to argue that automotive companies have built consensus through a dominant narrative that defines sustainability narrowly, which marginalizes or omits critical issues such as lithium and cobalt mining, slave and child labor associated with extraction of these minerals along with environmental degradation. We will be using qualitative analysis to examine the archival data we gathered. Based on the nature of the findings, we may also use quantitative analysis.”

Criteria for Applicants:

• Quantitative Analysis of Archival Data
• *Conduct content analysis to quantify the frequency of specific themes or keywords in the archival data
• *Perform basic statistical analysis of coded data to identify trends or correlations
• Create charts and graphs to represent quantitative findings visually.
• Advanced-Data Processing:
• *Leveraging an undergraduate's higher-level computing skills can enhance data processing:
• *Develop scripts or macros to automate repetitive data processing tasks
• *Use text mining techniques to extract relevant information from large volumes of archival data
• *Apply natural language processing methods to analyze textual data for sentiment or topic modeling
• Knows Python or similar programming language

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