ATLAM – The Audiovisual Transfer in Latin American Migration
ATLAM is a 4-year PhD research project (2023-2027) conducted at Ghent University within the research groups of Multiples, TRACE and CESSMIR.
The project aims to shed light on the audiovisual transfer of transit migrants’ sense of identity and belonging, examining its role in perpetuating, challenging, mitigating or retranslating existing discourses on this matter, as well as its potential to engender new discourses.
Fields of Research
- Digital Migration Studies
- Critical Discourse Studies
- Translation Studies
- Sociolinguistics
This project explores the process of transnational identity construction among Latin American migrants in transit through Mexico. Facing significant challenges in an increasingly hostile political climate towards migration, migrants actively seek to build new communities along the way, while maintaining close ties with their homeland. The first research question (RQ1) underpinning this project is whether this endeavour aligns with the concept of Latinidad – a relatively novel form of transnational identity and belonging, offering an alternative to conventional state citizenship. A second research question (RQ2) delves into the impact of digitalisation on the expression of identity among transit migrants, focussing specifically on their engagement with audiovisual platforms such as Youtube, Instagram, and Facebook, and examining how their use of these platforms intertwines with their ongoing identity construction while in transit. A third research question (RQ3) probes whether these platforms serve as digital spaces for migrants to engage in acts of (self-)representation and (self-)translation, potentially drawing on their new sense of self to craft counter-narratives to the prevalent anti-immigrant rhetoric and Othering discourses pervasive in mainstream media.
To answer these research questions, this study takes a mixed-methods approach by combining desk research for RQ1, ethnographic fieldwork for RQ2, and critical and multimodal discourse analysis for RQ3.