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The implementation of income-generating still faces problems, such as the lack of well-established internal control and differences in implementation in each unit. This study aims to analyze internal controls, financial viability (FV) and leadership qualities (LQ) in the implementation of income-generating in Indonesian higher education.

This study is quantitative and uses a causal approach. The population of this research is the unit leader and the person in charge of the activity that generates income, with a total sample of 111 people. The sampling technique used is simple random sampling. Data were analyzed using moderation regression analysis (MRA) with the WrapPLS (partial least square) analysis tool.

The results indicate that internal control and FV significantly affect the management of income-generating. The existence of LQ as a moderating variable can moderate and weaken the influence of internal controls and FV on the management of income-generating. In this finding, the unit leader and the person in charge of activities that generate income in higher education need to improve managerial skills, including ethics, uphold integrity, clear vision, quick adaption, honestly and trust so that the management of income-generating can achieve higher education goals more effectively and efficiently.

This research shows that universities need to create a good environment to build an ecosystem that can improve the management. The university encourages the good management by strengthening the leadership. However, the research has a limitation: the study was only conducted in one state university.

The implementation of income generation in the public financial management system of legal entity universities requires accountability for sources of income so that internal controls and the role of finance are needed to ensure the continuity of universities.

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.

This aim of this study is to identify the ways of helping public authorities bring about change to environmentally sustainable household food practices.

The authors identified the practices involved in this concept from the consumer perspective and measured their diffusion among French households. The analyses were conducted following two successive data collection campaigns comprising 571 and 501 respondents in France. The methodology involved two complementary scaling techniques: factor analysis and item response theory.

The results show that consumers understand sustainable food through five food practices: buying and cooking products with sustainable attributes, anti-waste storage, self-production, plant protein consumption and anti-waste cooking.

The findings suggest that while at the individual level people appear to have incorporated anti-waste practices into their daily lives, at the household level, there is still work to be done for improving diets and stimulating the production of home-grown food. It is also worth noting that the emerging vision typically involves sustainable foods that are organic, locally grown, seasonal, based on fair trade and packaging-free.