1,720,958 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Optimization method of conditioning factors selection and combination for landslide susceptibility prediction
Full text linkLandslide susceptibility prediction (LSP) is significantly affected by the uncertainty issue of landslide related conditioning factor selection. However, most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue. Targeted, this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP, and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors. An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors. Five commonly used factor selection methods, namely, the correlation analysis (CA), linear regression (LR), principal component analysis (PCA), rough set (RS) and artificial neural network (ANN), are applied to select the optimal factor combinations from the original 29 conditioning factors. The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models, such as CA-multilayer perceptron, CA-random forest. Additionally, multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of “accurate data, rich types, clear significance, feasible operation and avoiding duplication” are constructed for comparisons. Finally, the LSP uncertainties are evaluated by the accuracy, susceptibility index distribution, etc. Results show that: (1) multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models; (2) Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods. Conclusively, the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes. In contrast, a satisfied combination of conditioning factors can be constructed according to the proposed principle
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
A hierarchical graph-based hybrid neural networks with a self-screening strategy for landslide susceptibility prediction in the spatial–frequency domain
No full textLandslide susceptibility prediction (LSP) is a complex task with unresolved uncertainties, such as errors in sample classification and intricate relationships among environmental factors and spatial grid units. Additionally, the absence of interpretable black box models restricts the credibility and effectiveness of prediction models. To tackle these problems, an innovative interpretable deep learning model based on self-filtering graph convolutional networks and long short-term memory (SGCN-LSTM) is proposed. In the SGCN-LSTM, a self-screening strategy is employed to remove landslide/non-landslide samples with substantial errors that fall outside a defined threshold interval. Furthermore, SGCN-LSTM extracts nonlinear connections between environmental factors and long-range dependencies among grid units through spatial nodes and information gates. The Anyuan County in south China, with 2,655,972 grid units, 16,594 labeled, served as the study area. The LSP models used numeric inputs from the Frequency Ratios of 10 environmental factors in these spatial grid units. Results show that the accuracy and area AUC of the SGCN-LSTM achieve 92.38% and 0.9782, which are higher than those of one deep learning model cascade-parallel long short-term memory and conditional random fields (by 5.88% and 0.0305), and four machine learning models (by 12.44-20.34% and 0.0532–0.1909). This article delves into SGCN-LSTM ‘s evaluation results using the SHAP method, providing insights into the landslide development patterns and spatial heterogeneity of associated environmental factors in Anyuan County, with a global interpretability perspective. In conclusion, the SGCN-LSTM automatically screens erroneous samples, effectively extracts nonlinear features and spatial relationships from various environmental factors and delivers superior prediction accuracy and robustness for LSP
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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