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In this chapter, we reflect on how we frame our research on international scholarship programs within the field of comparative and international education and identify perspectives that influence our research. We also briefly describe the theories that shape our research: human capital theory and sociological perspectives that emphasize the centrality of context. We discuss emerging research on international scholarship programs and identify fruitful future directions for comparative and international research on higher education.

Higher education researchers are often challenged by the difficulty of empirically validating causal links posited by theories or inferred from correlational observations. The instrumental variable (IV) estimation strategy is one approach that researchers can use to estimate the causal impact of various higher education–related interventions. In this chapter, we discuss how the body of quantitative research specifically devoted to higher education has made use of the IV estimation strategy: we describe how this estimation strategy was used to address causality concerns and provide examples of the types of IVs that were used in various subfields of higher education research. Our discussion is based on a systematic review of a corpus of econometric studies on higher education–related issues that spans the last 30 years. The chapter concludes with a critical discussion of the use of IVs in quantitative higher education research and a discussion of good practices when using an IV estimation strategy.

The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.

The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.

The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.

Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.