1,721,034 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
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
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Using Reinforcement Learning to Personalize Dosing Strategies in a Simulated Cancer Trial with High Dimensional Data
No full textIn a simulation of an advanced generic cancer trial, I use Q-learning, a reinforcement learning algorithm, to develop dynamic treatment regimes for a continuous treatment, the dose of a single drug. Selected dynamic treatment regimes are tailored to time-varying patient characteristics and to patient subgroups with differential treatment effects. This approach allows estimation of optimal dynamic treatment regimes without a model of the disease process or a priori hypotheses about subgroup membership. Using observed patient characteristics and outcomes from the simulated trial, I estimate Q-functions based on 1) a single regression tree grown by the Classification And Regression Trees (CART) method, 2) random forests, and 3) a slightly modified version of Multivariate Adaptive Regression Splines (MARS). I then compare the survival times of an independent group of simulated patients under treatment regimes estimated using Q-learning with each of the three methods, 10 constant dose regimes, and the best possible treatment regime chosen using a brute force search over all possible treatment regimes with complete knowledge of disease processes and their effects on survival. I also make these comparisons in scenarios with and without spurious high dimensional covariates and with and without patient subgroups with differential treatment effects. Treatment regimes estimated using Q-learning with MARS and random forests greatly increased survival times when compared to the constant dose regimes, but were still considerably lower than the best possible dose regime. Q-learning with a single regression tree did not outperform the constant dose regimes. These results hold across high dimensional and subgroup scenarios. While the MARS method employed produces much more interpretable models than random forests, and therefore has more promise for patient subgroup identification, I show that it is also more sensitive to variations in training data
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A Nonparametric Multiple Imputation Approach For MNAR Mechanism Using the Sample Selection Model Framework
No full textMissing data is an unavoidable issue when performing data analysis. If the missing probability is related to unobserved variables, missingness is considered as missing not at random (MNAR). It is challenging to analyze data subject to MNAR. There are two ways to induce MNAR mechanism: sample selection model and pattern mixture model. Under the sample se-lection model framework, we develop a nonparametric multiple imputation (NNMI) method to estimate the marginal mean of an outcome subject to MNAR mechanism, where the sample se-lection model is only used as a working model to define the imputing set for each missing indi-vidual. We perform simulation studies to compare the performance of the proposed approach with a parametric multiple imputation approach, which directly uses the sample selection model to impute missing individuals. The results show that our method performs well for data subject to MNAR mechanism. Due to the limitations of current estimation method, we have not found solid proof to demonstrate the proposed method has better performance than parametric multi-ple imputation to handle MNAR when the sample selection model is misspecified. We also ap-ply the proposed approach to a real dataset to estimate the marginal mean of HbA1c level for carotid patients, whose HbA1c is subject to missingness
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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