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AbstractThe sustainable development goals (SDGs) are a blueprint for achieving a better and more sustainable future for all by defining priorities and aspirations for 2030. This paper attempts to expand on the United Nations SDGs definition by leveraging the interrelationship between science and technology. We utilize SDG classification of scientific publications to compile a machine learning (ML) model to classify the SDG relevancy in patent documents, used as a proxy of technology development. The ML model was used to classify a sample of patent families registered in the European Patent Office (EPO). The analysis revealed the extent to which SDGs were addressed in patents. We also performed a case study to identify the offered extension of ML model detection regarding the SDG orientation of patents. In response to global goals and sustainable development initiatives, the findings can advance the identification challenges of science and technology artefacts. Furthermore, we offer input towards the alignment of R&D efforts and patenting strategies as well as measurement and management of their contribution to the realization of SDGs.

AbstractThis paper presents TeknoAssistant, a domain-specific tech mining method for building a problem–solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naïve Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either “problem”, “solution” or “none” categories, and a problem–solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process.

The doi, interdisciplinarity and time-series data of the full samples this manuscript used.

AbstractSince the borders of disciplines change over time and vary across communities and geographies, they can be expressed at different levels of granularity, making it challenging to find a broad consensus about the measurement of interdisciplinarity. This study contributes to this debate by proposing a journal specialization index based on the level of repetitiveness of keywords appearing in their articles. Keywords represent one of the most essential items for filtering the vast amount of research available. If chosen correctly, they can help to identify the central concept of the paper and, consequently, to couple it with manuscripts related to the same field or subfield of research. Based on these universally recognized features of article keywords, the study proposes measuring the specialization of a journal by counting the number of times that a keyword is Queryrepeated in a journal on average (Sj). The basic assumption underlying the proposal of a journal specialization index is that the keywords may approximate the article’s topic and that the higher the number of papers in a journal based on a topic, the higher the level of specialization of that journal. The proposed specialization metric is not invulnerable to a set of limitations, among which the most relevant seems to be the lack of a standard practice regarding the number and consistency of keywords appearing in each article.

AbstractWe compare Covid-related working papers in economics to non-Covid-related working papers in four dimensions. Based on five well-known working papers series and data from the RePEc website, we find that Covid papers mainly cover topics in macroeconomics and health, they are written by larger teams than non-Covid papers, are more often downloaded and they receive more citations relative to non-Covid papers.

AbstractThe motivation for our research is the view, widespread among Polish scientists, that under the Communist Party’s rule it was always necessary to refer to Marx, Engels, Lenin or Stalin (we call them ‘classics’), especially in the highly-politicised fields like humanities and social sciences, in order for the work to pass the censorship procedures and be published. Therefore, in this paper, we aim to determine whether the 'classics' were commonly cited in a formally socialist country under the rule of the Communist Party (Polish Workers' Party/Polish United Workers’ Party). To address the main research question, we use the Citation Index of the History of Polish Media that covers all publications, whether scholarly articles or book publications, on the history of Polish media; in total, 6880 publications and 59,827 citations from the 1945‒2009 period. We found that citations of the works of the ‘classics’ (N = 296) constitute 0.49% of all citations in the database used and that the practice of citing the 'classics' was extremely rare (just 64 occurrences in the analysed sample). Our research also contributes to the development of reflection in historical bibliometrics and argues that bibliographical databases need to cover various types of publications, especially scholarly book publications, written in different languages (not only in English).