• Digital Humanities and Cultural Heritage
• Open Access close
• Publications close
• Research data close
• Research software close
• Other research products close
• Article close
• Other literature type close
• Scientometrics close

AbstractIn this paper, we examine how a research institution’s social structure and the presence of academic opinion leaders shaped the early adoption of a scientific innovation. Our case considers the early engagement of mathematical economists at the Cowles Commission with John von Neumann and Oskar Morgenstern’sTheory of Games and Economic Behavior.We argue that scholars with administrative leadership functions who were not only scientifically but also organizationally central—in our case Jacob Marschak, the director of research at Cowles—played a crucial role in promoting the early adoption of theTheory of Games. We support our argument with a scientometric analysis of all acknowledgments made in 488 papers published from 1944 to 1955 in the two main research paper series at the Cowles Commission. We apply blockmodeling techniques to the acknowledgments network to reconstruct the formal and informal social structure at Cowles at the time. Our case study emphasizes the importance of formal and informal social structures and the research agendas of academic opinion leaders to explain the early engagement with and adoption of innovative scientific ideas. Studies of the early adoption of scientific theories can benefit from complementary perspectives on the role of academic opinion leaders and scientists in explaining theory adoption.

AbstractWe have a limited understanding of the role emotions play in academia, as exploring emotions consistently and comparably is challenging due to the powerful influence of contextual factors. However, we have identified an interesting setting to empirically investigate the emotional response in academia by examining Nobel Prize winners. Scientists who aspire to earn a Nobel Prize are under pressure from their environment if they have not yet received the Prize. While there are various indicators that suggest the Nobel Prize is forthcoming, the question of “when” weighs heavily on the minds of leading candidates. Consequently, waiting for the Nobel Prize is emotionally taxing. We therefore hypothesize that Nobel laureates who have experienced a prolonged wait for the award would feel a stronger sense of relief upon receiving it. We are interested in measuring their level of emotionality after receiving the Nobel Prize by analyzing their banquet speeches using linguistic content analysis. Banquet speeches provide a consistent and controlled setting to compare emotionality across scientists and over time, as we can measure the same responses to the same recognition under the same circumstances. We expect that waiting longer for the Nobel Prize will increase the positive emotionality of Nobelists’ speeches. The waiting time is determined by calculating the years since the Nobel Prize-winning work was performed. By conducting this time-lapse study, we find a robust positive correlation between waiting time and positive emotions in Nobel Prize banquet speeches. We conclude that scientists who waited longer for the Nobel Prize reported higher levels of positive emotions during their speeches.

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.