An Analysis of Publications on Climate Change Communication Using a Bibliometric Lens

The effectiveness of climate change communication is incommunicado when there is a lack of comprehensive efforts to increase public knowledge and encourage proactive steps. The main objective of this study is to examine the evolving trends in publication and the developmental trajectory within climate change communication research, development, and publications. This study utilised datasets from the Scopus and Web of Science (WoS) databases, which were further analysed using the ScientoPy and VOSviewer. The findings suggest that there has been a notable increase in the number of publications since 2008, particularly in the WoS database, indicating a growing recognition and a more focused endeavour by researchers to delve into climate change communication. The most frequent keywords employed by past researchers were “Climate Change”, “Climate Change communication”, and “Climate communication”. The emergence of keywords such as “social media”, “science communication”, “environmental communication”, “framing”, and “climate action” in the year 2020 onwards signifies their recent prominence. Examining research growth and trends in climate change communication provides valuable insights into the advancements, recurring topics, and prominent individuals within this discipline. In a nutshell, the current study highlights the significance of proficient communication in tackling the intricate issues associated with climate change that can be a reference to potential readers and future researchers keen on this domain.A eficácia da comunicação sobre mudanças climáticas fica comprometida quando há uma falta de esforços abrangentes para aumentar o conhecimento público e incentivar medidas proativas. O principal objetivo deste estudo é examinar as tendências evolutivas na publicação e a trajetória de desenvolvimento na pesquisa, desenvolvimento e publicações sobre a comunicação de mudanças climáticas. Este estudo utilizou conjuntos de dados das bases Scopus e Web of Science (WoS), que foram posteriormente analisados utilizando ScientoPy e VOSviewer. Os resultados sugerem que houve um aumento notável no número de publicações desde 2008, especialmente na base de dados WoS, indicando um reconhecimento crescente e um esforço mais focado por parte dos pesquisadores para explorar a comunicação de mudanças climáticas. As palavras-chave mais frequentemente empregadas por pesquisadores anteriores foram “Mudanças Climáticas”, “Comunicação de Mudanças Climáticas” e “Comunicação Climática”. A emergência de palavras-chave como “mídias sociais”, “comunicação científica”, “comunicação ambiental”, “enquadramento” e “ação climática” a partir do ano de 2020 sinaliza sua recente proeminência. Examinar o crescimento e as tendências da pesquisa em comunicação de mudanças climáticas fornece insights valiosos sobre os avanços, temas recorrentes e indivíduos proeminentes dentro desta disciplina. Em suma, o estudo atual destaca a importância de uma comunicação eficiente para enfrentar os problemas complexos associados às mudanças climáticas, podendo servir como referência para leitores em potencial e futuros pesquisadores interessados nesse domínio

Mapping the impact of papers on various status groups in excellencemapping.net: a new release of the excellence mapping tool based on citation and reader scores

In over five years, Bornmann, Stefaner, de Moya Anegon, and Mutz (2014b) and Bornmann, Stefaner, de Moya Anegón, and Mutz (2014c, 2015) have published several releases of the www.excellencemapping.net tool revealing (clusters of) excellent institutions worldwide based on citation data. With the new release, a completely revised tool has been published. It is not only based on citation data (bibliometrics), but also Mendeley data (altmetrics). Thus, the institutional impact measurement of the tool has been expanded by focusing on additional status groups besides researchers such as students and librarians. Furthermore, the visualization of the data has been completely updated by improving the operability for the user and including new features such as institutional profile pages. In this paper, we describe the datasets for the current excellencemapping.net tool and the indicators applied. Furthermore, the underlying statistics for the tool and the use of the web application are explained

Identifying potentially excellent publications using a citation-based machine learning approach

Excellent research papers are vital to science and technology advances. Thus, the early identification of potentially excellent research papers and recognizing their value in science and technology is high on the research agenda. This study used a set of 5 static and 8 time-dependent citation features to explore six machine learning methods and identify the method with the best performance to identify potentially excellent papers. The study modelled Random Forest, LightGBM, Naive Bayes, Support Vector Machine, Neural Network, and TabNet to identify PEPs in the artificial intelligence field. The study defined highly cited papers using the threshold of the top 1% and top 5% and collected the data from the Web of Science®. Bibliometric and citation data from 485,041 research articles, proceeding papers, and reviews published in AI between 1990 and 2010 were collected initially. The data was screened and processed, and the final dataset consists of 96,169 papers for the training and test sets. The findings suggest that the time-dependent citation features are more important than the static features, and citation peak features are more significant than the citation features in identifying potentially excellent papers. The findings demonstrate the effect of threshold on machine learning outcomes (e.g., the top 1% and 5%); therefore, the study argues that the decision about threshold selection should be carefully made. LightGBM and Random Forest both performed with the given conditions and achieved the same score in accuracy and recall. Nevertheless, when comparing their performance in other indicators, such as F1 and cross-entropy loss, LightGBM performed better. The study concluded that LightGBM was the best-performing model for identifying potentially excellent papers. The papers identified the contributions and recommended future research

In Search of Excellent Research Assessment

This book provides analysis of current trends in research evaluation worldwide and compares the research assessment and innovation ecosystems in Austria, Bulgaria, the Czech Republic, Hungary, Lithuania, the Netherlands, Poland and Slovenia. It argues that in each country the research assessment system is interdependent with the national innovation system and the overall institutional governance/enforcement.Das Buch bietet eine Analyse aktueller Trends in der Forschungsbewertung weltweit und vergleicht die Forschungsbewertungs- und Innovationsökosysteme in Österreich, Bulgarien, der Tschechischen Republik, Ungarn, Litauen, den Niederlanden, Polen und Slowenien. Es wird argumentiert, dass das Forschungsbewertungssystem vom nationalen Innovationssystem und der gesamten institutionellen Governance/Durchsetzung abhängig ist