1,720,975 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Navigating the Paradox: Climate Change, Cutting-Edge Technologies, and Groundwater Sustainability
Get PDFThis article explores the paradoxical relationship between climate change, advanced technologies, and groundwater sustainability. It highlights how emerging technologies like artificial intelligence, blockchain, and the Internet of Things (IoT) offer innovative solutions for optimizing groundwater management while addressing climate change impacts. However, the chapter also warns of the environmental risks associated with these technologies, particularly their energy consumption and e-waste generation, which can further exacerbate climate challenges. The chapter examines practical applications such as desalination, precision farming, and water harvesting, evaluating their contributions to groundwater management and their environmental footprints. It argues that the net impact of these technologies depends largely on their design, implementation, and governance frameworks. The research identifies best practices to maximize benefits while minimizing negative environmental consequences. This work addresses key issues of water scarcity and the need for sustainable water supplies in a changing climate. It underscores the importance of fresh water for essential industries, including agriculture, energy production, and mineral processing, while acknowledging the profound effects of climate change and societal shifts on traditional water sources. The chapter also discusses the risks associated with technological investments in water management, such as toxic waste emissions, geopolitical tensions, and corruption. It emphasizes that emissions from these processes contribute significantly to rising atmospheric temperatures and water vapor levels, intensifying climate change. The chapter concludes by advocating for a holistic approach to water management, balancing the costs, benefits, and risks of emerging technologies. It highlights the potential of green engineering advancements and efficient water treatment methods, such as desalination and cleaner urban designs, to sustainably provide fresh groundwater for various uses. The chapter integrates data analytics from engineering and public health performance metrics to establish safe industry targets and calls for responsible governance to ensure technologies contribute positively to both groundwater sustainability and climate change mitigation
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa
No full textThe introduction of a deep learning-based method for non-destructive leaf area index (LAI) assessment has enhanced rapid estimation for wheat and similar crops, aiding crop growth monitoring, water, and nutrient management. Convolutional Neural Network (CNN)-based algorithms enable accurate, non-destructive quantification of seedling leaf areas and assess LAI across diverse genotypes and environments, demonstrating adaptability. Transfer learning, known for efficiency in plant phenotyping, was tested as a resource-saving approach for training the wheat LAI model. These advancements support wheat breeding, facilitate genotype selection for varied environments, accelerate genetic gains, and enhance genomic selection for LAI. By capturing diverse environments, this method can improve wheat resilience to climate change. Additionally, advances in machine learning and data science enable better prediction and distribution mapping of global wheat rust pathogens, a major agricultural challenge. Accurate risk identification allows for timely and effective control measures. Moreover, wheat lodging prediction models using CNNs can assess lodging-prone varieties, influencing selection decisions to improve yield stability. These artificial intelligence-driven techniques contribute to sustainable crop growth and yield enhancement, especially in the context of climate change and increasing global food demand
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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