This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation.
Ritual is one of the most classic research topics in the field of Anthropology, and rituals have close connection with medial practice. However, the research on this topic from the experience of Traditional Chinese Medicine is limited. This paper presents the whole story that a patient suffering from infertility got cured got cured by a doctor of Traditional Chinese Medicine(TCM) and finally became a mother. With the detailed description of each medical practice, including pulse-taking, traditional Chinese herb therapy, and postpartum confinement, this paper analyzes the ritualized elements in the whole process, interprets how ritual play a role in the practice of TCM, and points out ritual’s essential significance in contributing to human’s well being and adjusting the relationships between individual and the world.
PurposeThis study aims to track the historical development in tourism and hospitality research over the past 30 years by applying a novel interdisciplinary approach, combining both corpus linguistics and bibliometric analysis.Design/methodology/approachMost frequently discussed topics and newly emerging topics were identified by investigating 18,266 abstracts from 18 leading tourism and hospitality journals with corpus linguistics toolkit AntConc and natural language processing (NLP) tool spaCy. Trend analysis and bibliometric methods were used to determine the longitudinal changes of research topics, most highly-cited publications and authors' production.FindingsThis study revealed the evolution patterns of the identified 576 most frequently discussed topics across the four subperiods (1991–2000, 2001–2010, 2011–2015 and 2016–2020). Specifically, results showed that information technology-related topics account for the largest proportion of the identified 38 newly emerging topics from 2011. Besides, researchers are increasingly focusing on the use of more sophisticated and advanced statistical methodologies.Practical implicationsThis study helps researchers make sensible decisions on what research topics to explore; it also helps practitioners and stakeholders make the shift and track opportunities in the field.Originality/valueNo other studies have employed the novel interdisciplinary approach, combining corpus linguistic tools in linguistics, NLP techniques in computer science and bibliometric analysis in library and information science, for exploring research trends in tourism and hospitality.
Accounting is a routine activity. Through repetition, the scribes of the Ebla Archives (Syria, 24th cent. BCE) have been able to record thousands of transactions. They organized and stored accounting data referred to more than thirty years of the Palace G activities. The recurring textual patterns characterizing the administrative corpus are a byproduct of this routine-based approach. The ability to see recurring patterns in the textual record is fundamental when dealing with an administrative corpus: however, this ability fails when the patterns are buried in data. In this paper, I argue that theoretical aspects of data mining are not far from theoretical and methodological tenets of the historical approach. Data mining is a useful technique for the identification of document clusters and relevant information which would otherwise remain hidden. Furthermore, textual pattern recognition is critical to address topics such as the study of society: belonging to a category of complex problems, any socio-historical investigation requires dealing with multiple interconnected variables. However, not all research topics require such an approach. I define the line beyond which digital approaches are extremely useful (if not indispensable) as 'visibility threshold’. The position of this interface is relative and subjective.
This study evaluates institutional research performance in benchmark technological universities in Taiwan through intelligent research databases (SciVal) in digital libraries with Ministry of Education open data to explore the performance of research indicators and the research trend of topic clusters to ascertain accountability for decision makers. The research performance of eight benchmark technological universities in Taiwan is compared in this study. In addition, the trends in research topics in the top 10% of journals are explored. Descriptive statistics, correlation, ANOVA, and the Boston Consulting Group matrix were used in this study. Research personnel, publications, productivity, total citations, number of international collaborations, and academic research income in 2018 significantly positively correlated with each other. From 719 records of research topics, topic clusters and school types are the significant factors in research outputs. Biosensors, electrodes, and voltammetry are the leading topic clusters in the research trend. The topic cluster of decision-making, fuzzy sets, and models has the best growth rate in the SciVal results. This analysis provides useful insights to policymakers to improve institutional administration and research resource allocation.
Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.
AbstractIn recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing.
The chapter presents a rationale for using visual ethnography as part of the methodology in qualitative research and illustrates what visual ethnography methodology is capable of accomplishing when imagery is included in the investigative process. Visual ethnography offers a venue for collecting and analyzing data that would otherwise be inaccessible and positions imagery as an important, rather than a minimal or occasional, choice for use in qualitative research. Topics include contemporary definitions of visual ethnography and its value in qualitative research, historical applications of visual ethnographic theory that influence the way researchers view visual ethnography today, and contemporary uses of visual ethnography in data collection and analysis. Finally, the conclusion explores the future of visual ethnography.
This issue of Seed Science and Technology is a good reflection of the wide scope of the field of study. Species of interest include major cultivated crops as well as wild and native species. Likewise, research topics span a wide array of subjects relevant to those interested in basic seed biology, production, testing, ecology, conservation and biodiversity. Understanding basic mechanisms of seed dormancy and germination remains a major topic of interest. Seed quality and its attributes are also of particular interest, as evidenced by research articles on seed vigour, health, genetic purity and physical characterisation.