1,721,016 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
Studies on advanced natural language processing representations and deep learning in decision making systems
No full textIn our analysis, we must often rely on unstructured data --- for instance, written in natural language. In recent decades, the proliferation of openly available textual data has enabled the natural language processing research community to move from rule-based systems to statistical methods and, more recently, to neural methods. Despite these inspiring developments, there are some areas where the potential has not yet been fully realised.
This dissertation examined representations of textual information in data-driven educational and market analyses. A common denominator is the focus on scenarios with limited data availability and the use of previously underutilised information, such as: joint topic sentiment labels in cryptocurrency discussions, parse trees of mathematical formulas in predicting exercise levels, or machine uncertainty scores in question difficulty estimation. Five studies were published in journals or presented at international conferences.
The first study investigated how current Transformer architectures can give us insight into the difficulty of exam questions. Our experiments showed promising results, suggesting that model uncertainty can be successfully used to estimate question difficulty from text. The advantage of the proposed approach is that it can predict the relative difficulty of questions in different domains without having already calibrated questions or previous answers.
In the second study, we extended the automated feature engineering methods for text analysis to the cryptocurrency domain. We applied aspect-based sentiment analysis methods (JST and TS-LDA) and investigated the utility of subjectivity scores. Experiments were conducted on a new dataset that included multiple textual data sources, including news and forum data, which were previously underrepresented in the field. The added JST and TS-LDA features improved prediction performance compared to traditional LDA or remained on par in most experiments. At the same time, the extracted topics provided more detailed insight than most previous work.
The third study showed that it is possible to predict whether students will pass the course using only their navigation patterns in the online learning platform, without the previous grades. We introduced a novel and easy-to-implement percentage progress coding scheme that captures course structure and improves prediction performance by up to 10% in ROC AUC while scaling well to new courses and exercises.
The fourth study evaluated an adaptation of a natural language processing technique from machine comprehension to educational data mining. We enriched content representation by parsing mathematical formulas into syntax trees and embedding them with neural networks. Our experiments validated the approach using publicly available datasets and showed that incorporating syntactic information can improve performance in predicting the difficulty of a task.
The fifth and final study described an approach based on deep semantic learning to improve the quality of personalization in educational content recommendation systems. It also presented a system that moves to a more contextualised use of word meanings. This study laid the foundation for further ongoing research by the team
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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