Refugee integration in scholarship

The project also maps inequalities in knowledge production, examining where research is conducted, who produces it, which populations and topics receive attention, and how research is funded. A further strand evaluates how computational and AI-assisted methods can support large-scale evidence synthesis while maintaining transparency and researcher oversight.

About the project

Integration is one of the most widely utilised concepts research on refugee settlement and adaptation, yet there is no agreement about what integration means and how it can be defined.

Scholars seek to understand integration through employment, education, housing and health; social relationships and participation; cultural change and language; rights and citizenship; or experiences such as belonging and recognition. Related concepts, including assimilation, acculturation, adaptation, settlement and social inclusion, overlap with integration but are not always clearly distinguished and are sometimes used interchangeably.

These differences matter because the concepts researchers use shape what is measured, which experiences become visible, whether studies can be compared and what evidence is available to policymakers and practitioners.

The project moves beyond asking whether refugees integrate to examine how the concept of integration itself has been constructed and studied.

Key objectives

The project has three main objectives:

• Mapping integration-related concepts
  To examine how integration and related concepts are defined, operationalised and related to one another across scholarship on refugee settlement and adaptation.
• Mapping the structure of the research field
  To identify patterns in research geography, authorship, disciplines, methods, funding, populations and themes, and to examine gaps and inequalities in knowledge production.
• Testing computational and AI-assisted research methods
  To assess how active learning, text search and large language models can support large-scale systematic reviews, and where researcher interpretation and validation remain essential.

What does the research involve?

The project uses a systematic scoping review combining quantitative mapping of the research field with qualitative full-text conceptual analysis.

Searches of Scopus and Web of Science produced a final dataset of 6,205 peer-reviewed journal articles published between 2001 and 2025. These are analysed to map patterns in publication, geography, authorship, discipline, methodology, funding, populations and research themes.

A purposively selected sample of 200 publications is analysed in greater depth using NVivo to examine how eight key integration-related concepts are defined, operationalised and related to one another. Text-search tools support the identification of relevant passages, while final coding and interpretation remain researcher-led.

AI and methodological innovation

The project also examines how computational and AI-assisted methods can support large-scale evidence synthesis. Active learning was used to support screening, while a separate validation study compared researcher-led coding with large language model classification using the same predefined coding framework.

The analysis examines where the approaches converge and diverge, particularly in dealing with conceptual ambiguity and context. This contributes to wider debates about the transparent and responsible use of AI in systematic reviews and qualitative research.

Outputs

• Refugee Integration Research Explorer
• Dataset DOI
• Articles

Research team

• Professor Jenny Phillimore
• Professor Sin Yi Cheung [Cardiff]
• Dr Gabriella D’Avino

Funding

This research is supported by a British Academy and Leverhulme Trust Small Research Grant, grant reference SRG25\251753.