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The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.

The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.

Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.

The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.

The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.

The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.

These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

In this paper, we examine the comparative advantage of Korea and China while focusing on their technology level. The three digit SITC (Standard International Trade Classification) data is classified by technology level and the revealed comparative advantage (RCA) is derived from 1992-2009 by using UN COMTRADE data. For careful interpretation of the comparative advantage and technology levels, we also examined intra-industry trade and unit values of bilateral Korea-China trade, and semi-conductor industry technology. We found that the revealed comparative advantage has moved from low technology products to high technology products in Korea. China still maintains a comparative advantage in low technology products such as textiles and clothing, but at the same time, China’s high and medium-high technology products have recently gained a comparative advantage. The perception that China only has a comparative advantage for labor intensive products with low technology should be changed based on our analysis. However, China’s advancement in technology should not be overestimated. When comparing the unit value of basic materials of Korea’s and China’s exports, we found that Korea’s export product prices are on average higher than that of China’s, although the gap is reducing. A wider technology gap between Korea and China still exists in the semi-conductor industry, which is one of the most advanced high technology industries throughout the world.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

Previous dynamic prediction models rarely handle multi-period data with different intervals, and the large-scale patient hospital records are not effectively used to improve the prediction performance. This paper aims to focus on the prediction of cardiovascular disease using the improved long short-term memory (LSTM) model.

A new model based on the traditional LSTM was proposed to predict cardiovascular disease. The irregular time interval is smoothed to obtain the time parameter vector, and it is used as the input of the forgetting gate of LSTM to overcome the prediction obstacle caused by the irregular time interval.

The experimental results show that the dynamic prediction model proposed in this paper obtained a significant better classification performance compared with the traditional LSTM model.

In this paper, the authors improved the LSTM by smoothing the irregular time between different medical stages of the patient to obtain the temporal feature vector.

The purpose of this paper is to investigate how active labour market policy (ALMP) training programmes and hiring subsidies increase or decrease differences in the unemployment risk between lesser and higher educated people during an economic downturn. A focus is put on potential job competition dynamics and cumulative (dis)advantages of the lesser and higher educated.

The paper uses multi-level data. The fifth wave (2010) of the European Social Survey was used and combined with macro-level data on labour market policies of the OECD. The sample consisted of 18,172 observations in 19 countries.

The results show that higher levels of participation and spending on training policies are related to a smaller difference in the unemployment risks of the educational groups. Higher training policy intensity is associated with a lower unemployment risk for the lesser educated and a higher unemployment risk for the higher educated. This implies that the lesser educated are better able to withstand downward pressure from the higher educated, thereby, reducing downward displacement during an economic downturn. Hiring subsidies do not seem to be associated with the impact of education on unemployment.

The paper adds to the discussion on ALMP training and hiring subsidies that are primarily rooted in the human capital theory and signalling theory. Both theories ignore the social context of labour market behaviour. The job competition theory and cumulative (dis)advantage theory add to these theories by focussing on the relative position of individuals and the characteristics that accompany the social position of the individual.

Raising a child with learning and developmental disabilities in a low-income setting is challenged by inadequate resources, limited support and poverty. The impacts on caregivers include fatigue, distress and isolation. The purpose of this paper is to report on a programme (2008-2021) that was set up in Kilifi County, Kenya to investigate and address these difficulties.

The programme used mixed methods through a series of interconnected studies, starting with a situation analysis, followed by a home-based intervention where the caregiver served as agent for change using augmentative and alternative communication (AAC) methods. This was followed by two community-based inclusive development initiatives: disability awareness training to community groups and empowering self-help groups for caregivers of children growing up with disabilities.

The situation analysis revealed scarce support services for caregivers and children with learning and developmental disabilities, with report of limited resources, inadequate coverage and poor professional practice. A home-based, AAC intervention was associated with improved caregiver well-being, significant positive changes to caregiver perceptions of the child’s communication and some expansion to the child’ social activities. However, questions around sustainability persisted. Disability awareness training led by persons with lived experience of disability showed positive changes to the views, values and attitudes of established community groups. Caregiver participation in self-help groups was associated with their greater personal agency, perceptions of increased social support and reduced severity of child’s disability.

The programme narrative demonstrates a rationalised and evidence-based process for community-based inclusive development that is low cost, culturally acceptable, with potential for sustainability.

This study focusses on dependent self-employment, which covers a situation where a person works for the same employer as a typical worker whilst on a self-employment contractual basis, i.e. without a traditional employment contract and without certain rights granted to “regular” employees.

The research exploits the individual-level dataset of 35 European countries extracted from the 2017 edition of the European Labour Force Survey (EU LFS) and compares the characteristics of employees and dependent self-employed individuals. Methodologically, the study relies on the estimation of a multivariate logistic regression model.

The main hypothesis assuming that dependent self-employed work most often in low-skilled occupations was empirically supported. There was also a non-linear (u-shaped) relationship between the years of accumulated experience (with a turning point at 35 years) and the likelihood of being dependent self-employed. Other results showed that dependent self-employed are less likely to be women and the dependent self-employed are more likely born outside of the countries where the dependent self-employed participate in the labour markets.

The study contributes to the field by adopting a comparable definition of dependent self-employment and exploiting the recent theoretical support of The Work Precarity Framework. The phenomenon should still be addressed by policymakers and labour office representatives, aiming to protect, primarily, vulnerable lower-skilled workers. The ongoing research should study the longitudinal dimension of dependent self-employment with a focus on motivational aspects.

The purpose of this paper is to show how the documentation movement associated with the utopian thinkers Paul Otlet and Henri La Fontaine relied on patent offices as well as the documents most closely associated with this institutional setting – the patents themselves – as central to the formation of the document category. The main argument is that patents not only were subjected to and helped construct, but also in fact engineered the development of technoscientific order during 1895–1937.

The paper draws on an interdisciplinary approach to intellectual property, document theory and insights from media archeology. Focused on the historical period 1895–1937, this study allows for an analysis that encapsulates and accounts for change in a number of comparative areas, moving from bibliography to documentation and from scientific to technoscientific order. Primary sources include Paul Otlet’s own writings, relevant contemporary sources from the French documentation movement and the Congrès Mondial de la documentation universelle in 1937.

By understanding patent offices and patents as main drivers behind those processes of sorting and classification that constitute technoscientific order, this explorative paper provides a new analytical framework for the study of intellectual property in relation to the history of information and documentation. It argues that the idea of the document may serve to rethink the role of the patent in technoscience, offering suggestions for new and underexplored venues of research in the nexus of several overlapping research fields, from law to information studies.

Debates over the legitimacy and rationale of intellectual property have raged for many years without signs of abating. Universities, research centers, policy makers, editors and scholars, research funders, governments, libraries and archives all have things to say on the legitimacy of the patent system, its relation to innovation and the appropriate role of intellectual property in research and science, milieus that are of central importance in the knowledge-based economy. The value of this paper lies in proposing a new way to approach patents that could show a way out of the current analytical gridlock of either/or that for many years has earmarked the “openness-enclosure” dichotomy. The combination of intellectual property scholarship and documentation theory provides important new insight into the historical networks and processes by which patents and documents have consolidated and converged during the twentieth century.

The study aims to paint a representative picture of the current state of search interfaces of Swedish online museum collections, focussing on search functionalities with particular reference to subject searching, as well as the use of controlled vocabularies, with the purpose of identifying which improvements of the search interfaces are needed to ensure high-quality information retrieval for the end user.

In the first step, a set of 21 search interface criteria was identified, based on related research and current standards in the domain of cultural heritage knowledge organization. Secondly, a complete set of Swedish museums that provide online access to their collections was identified, comprising nine cross-search services and 91 individual museums' websites. These 100 websites were each evaluated against the 21 criteria, between 1 July and 31 August 2020.

Although many standards and guidelines are in place to ensure quality-controlled subject indexing, which in turn support information retrieval of relevant resources (as individual or full search results), the study shows that they are not broadly implemented, resulting in information retrieval failures for the end user. The study also demonstrates a strong need for the implementation of controlled vocabularies in these museums.

This study is a rare piece of research which examines subject searching in online museums; the 21 search criteria and their use in the analysis of the complete set of online collections of a country represents a considerable and unique contribution to the fields of knowledge organization and information retrieval of cultural heritage. Its particular value lies in showing how the needs of end users, many of which are documented and reflected in international standards and guidelines, should be taken into account in designing search tools for these museums; especially so in subject searching, which is the most complex and yet the most common type of search. Much effort has been invested into digitizing cultural heritage collections, but access to them is hindered by poor search functionality. This study identifies which are the most important aspects to improve.

The purpose of this study is, first, to determine which developments have shaped official hotel classification systems over recent years (including the impact of guest-review platforms) and second to establish the future of those systems through the eyes of the people who are actually in charge of operating them.

Semi-structured interviews were chosen as the most suitable method for approaching hotel classification system administrators. This method is in line with previous research on approaching key informants in their respective fields. Sixteen people representing 12 different official national hotel classification systems from across the world as well as one commercial hotel star rating system participated in the online interviews.

The first main conclusion is that hotel classification systems – especially voluntary ones – would not have survived the enormous impact of guest-review platforms without quickly adjusting to the ever-changing hotel industry landscape. The frequent review of classification criteria and procedures has become the main survival strategy of classification systems. The second conclusion is that system operators are strongly optimistic about the future outlook of hotel classification based on their proven flexibility to swiftly adapt to new market conditions.

Research about hotel classification systems is usually based on the views of the systems' users, i.e. hotels or hotel guests, whereas the present paper reflects the perspective of the systems' operators, an angle rarely analyzed in the literature.