Identifying Hidden Communities of Interest with Topic-based Networks: A Case Study of the Community of Philosophers of Science (1930-2017)

Scientific networks are often investigated by means of citation analyses. Yet, interpretation of such networks in terms of semantic (and often disciplinary) content heavily depends on supplementary knowledge, notably about author research specialties. Similar situations arise more generally in many types of social networks whose semantic interpretation relies on supplementary information. Here, author community net-works are inferred from a topic model which provides direct insights into the semantic specificity of the identified “hidden communities of interest” (HCoI). Using a philosophy of science corpus of full-text articles (N=16,917), we investigate its underlying communities by measuring topic profile correlations be-tween authors. A diachronic perspective is built by modeling the research networks over different time-periods and mapping genealogical relationships be-tween communities. The results show a marked in-crease in philosophy of science communities over time and trace the progressive appearance of the specialization areas that structure the field today

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Changing the Scholarly Sources Landscape with Geomorphology Undergraduate Students

Science is a core discipline in academia yet the focus of most undergraduate technical writing is generally on the data and results, not the literature review. The Science, Technology, Engineering, and Math (STEM) librarian and a new geology professor at the University of Nebraska at Omaha (UNO) collaborated to develop an information literacy session for students in a geomorphology class. Here we outline the background of the campus STEM initiatives and the assignment as well as the library instruction activity, learning outcomes, and assessment components. The activity improved student use of scholarly sources and we provide suggested activity modifications for future teaching and assessment efforts

An NLP Analysis of Health Advice Giving in the Medical Research Literature

Health advice – clinical and policy recommendations – plays a vital role in guiding medical practices and public health policies. Whether or not authors should give health advice in medical research publications is a controversial issue. The proponents of actionable research advocate for the more efficient and effective transmission of science evidence into practice. The opponents are concerned about the quality of health advice in individual research papers, especially that in observational studies. Arguments both for and against giving advice in individual studies indicate a strong need for identifying and accessing health advice, for either practical use or quality evaluation purposes. However, current information services do not support the direct retrieval of health advice. Compared to other natural language processing (NLP) applications, health advice has not been computationally modeled as a language construct either. A new information service for directly accessing health advice should be able to reduce information barriers and to provide external assessment in science communication. This dissertation work built an annotated corpus of scientific claims that distinguishes health advice according to its occurrence and strength. The study developed NLP-based prediction models to identify health advice in the PubMed literature. Using the annotated corpus and prediction models, the study answered research questions regarding the practice of advice giving in medical research literature. To test and demonstrate the potential use of the prediction model, it was used to retrieve health advice regarding the use of hydroxychloroquine (HCQ) as a treatment for COVID-19 from LitCovid, a large COVID-19 research literature database curated by the National Institutes of Health. An evaluation of sentences extracted from both abstracts and discussions showed that BERT-based pre-trained language models performed well at detecting health advice. The health advice prediction model may be combined with existing health information service systems to provide more convenient navigation of a large volume of health literature. Findings from the study also show researchers are careful not to give advice solely in abstracts. They also tend to give weaker and non-specific advice in abstracts than in discussions. In addition, the study found that health advice has appeared consistently in the abstracts of observational studies over the past 25 years. In the sample, 41.2% of the studies offered health advice in their conclusions, which is lower than earlier estimations based on analyses of much smaller samples processed manually. In the abstracts of observational studies, journals with a lower impact are more likely to give health advice than those with a higher impact, suggesting the significance of the role of journals as gatekeepers of science communication. For the communities of natural language processing, information science, and public health, this work advances knowledge of the automated recognition of health advice in scientific literature. The corpus and code developed for the study have been made publicly available to facilitate future efforts in health advice retrieval and analysis. Furthermore, this study discusses the ways in which researchers give health advice in medical research articles, knowledge of which could be an essential step towards curbing potential exaggeration in the current global science communication. It also contributes to ongoing discussions of the integrity of scientific output. This study calls for caution in advice-giving in medical research literature, especially in abstracts alone. It also calls for open access to medical research publications, so that health researchers and practitioners can fully review the advice in scientific outputs and its implications. More evaluative strategies that can increase the overall quality of health advice in research articles are needed by journal editors and reviewers, given their gatekeeping role in science communication

The impact of corporate governance on default risk: BERTopic literature review

This study utilizes the BERTopic methodology, a topic modelling tool that facilitates a meticulous exploration of existing literature, to comprehensively review the interplay between corporate governance and default risk. Through analysis of diverse empirical studies, it delves into understanding how corporate governance practices influence default probability. The study underscores the importance of effective governance mechanisms — board attributes, ownership structures, executive compensation, shareholder rights, and disclosure practices — in molding default probabilities. It also highlights the role of external governance mechanisms and regulatory frameworks in managing default risk. Notably, this research advocates for further investigation into emerging governance models and their integration with modern machine-learning techniques to amplify their impact

Digging by Debating: Linking massive datasets to specific arguments

We will develop and implement a multi-scale workbench, called "InterDebates", with the goal of digging into data provided by hundreds of thousands, eventually millions, of digitized books, bibliographic databases of journal articles, and comprehensive reference works written by experts. Our hypotheses are: that detailed and identifiable arguments drive many aspects of research in the sciences and the humanities; that argumentative structures can be extracted from large datasets using a mixture of automated and social computing techniques; and, that the availability of such analyses will enable innovative interdisciplinary research, and may also play a role in supporting better-informed critical debates among students and the general public. A key challenge tackled by this project is thus to uncover and represent the argumentative structure of digitized documents, allowing users to find and interpret detailed arguments in the broad semantic landscape of books and articles

What gets published and what doesn’t?:Exploring optimal distinctiveness and diverse expectations in entrepreneurship articles

The field of entrepreneurship has seen remarkable growth, increasing the expectations of academic audiences. Articles need to balance novelty with rigorous methodology, theoretical contributions, social implications, and coherent argumentation to succeed in the publication process. However, navigating these varied and sometimes conflicting expectations to achieve optimal distinctiveness in academic narratives is challenging for authors. To explore how authors can achieve optimal distinctiveness amidst these complex expectations, we studied academic narratives and related editorial decisions of two leading entrepreneurship journals, Entrepreneurship: Theory &amp; Practice (ETP, 4,151 papers) and Small Business Economics Journal (SBEJ, 4,043 papers), using computer-aided text analysis. Our study debunks common assumptions about what makes a successful entrepreneurship paper, providing an empirical basis for understanding actual versus perceived publication requisites. Furthermore, we extend optimal distinctiveness theory by demonstrating that high distinctiveness is not uniformly advantageous, meeting numerous expectations is not necessarily beneficial, and clear language is crucial for complex narratives. Our study underscores that crafting narratives is more nuanced than traditionally believed.</p