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The article deals with one of the key causes of Roma children's low academic achievement, notably their presence in segregated special schools originally reserved for mentally disabled children. The purpose of the research was to analyse the assessment process for school‐readiness and special educational needs, and discover the reasons for Roma children's widespread failure on the tests.

Using a quantitative survey and qualitative focus group interviews, the study analysed the assessment process for school‐readiness and special educational needs.

The tests in use offer an overly generalized picture of children's abilities. The test results have little influence on the actual decisions about the schools children will be sent to. Roma children tend to do significantly worse on the tests than non‐Roma children, in all examined areas.

The situation of Roma is similar in all countries of the East and Central European region, and resembles the situation of all socially excluded ethnic minority groups. The results can therefore contribute to a better understanding of the educational situation of Roma and other ethnic minority groups in the region. However, the education system as well as the process of determining special educational needs are in several ways unique in each country. The findings therefore have limited validity outside of Hungary.

Although the problems with Roma children's academic performance are well documented, there had been no research in Hungary that focused on the selection process and the problems of using assessment tests in determining Roma children's special educational needs.

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.