Societies and Cultures Institute (SCI)

Description

Led by world-class researchers across the Humanities, Arts and Social Sciences (HASS) communities, the Societies and Cultures Institute (SCI) tackles issues of immediate global concern through challenge-based interdisciplinary research. SCI collaborates with academic and non-academic partners across the HASS and the Science, Technology, Engineering, Mathematics and Medicine (STEMM) disciplines to lead innovative, multi-faceted programmes of research, education and knowledge exchange.

SCI aims are:

• To encourage and enable innovation in HASS-based research
• To enhance the visibility of HASS-based research
• Develop the skills and tools, knowledge and understanding necessary to address present social and cultural priorities

At the Societies and Cultures Institute, members believe that HASS-led (Humanities, Arts and Social Sciences) research is vital to defining, analysing and finding solutions to existing and future global challenges. SCI guiding principle is to develop and support early-stage, innovative, ambitious and interdisciplinary research grounded in the knowledge and methods of HASS disciplines yet building productive collaborations across the research community, including collaboration in STEMM, to address present challenges and future opportunities. All research supported will have clear impact potential of cultural, social, economic or policy value. In addition, members will identify priority areas for targeted development support on a biennial cycle. For 2022/23 and 2023/24 the priority areas will be:

• AI and HASS
• The social and cultural demands for natural environment (Environmental Humanities) and plant research (called ‘Plant Humanities’ by UKRI-AHRC)
• Equalities and justice policy and practice with particular focus on the Global South

In practice, the Institute's AI and HASS priority strand includes joint programming with the University's Institute for Data Science and Artificial Intelligence, such as a research showcase on how large language models process idiomatic and multiword expressions. That session set out how large language models capture specific word usages but struggle with common idioms whose meaning cannot be read from the individual words, and how advances in word representations have affected the identification and modelling of such non-literal language, a recognised difficulty for natural language processing and machine translation. This work reflects the Institute's engagement with AI methods where they intersect with humanities and social science research on language and communication.

Offers funding

No, this infrastructure does not provide funding.

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University of Exeter
Exeter