1,720,959 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
Nonparametric Bayesian Methods for Extracting Structure from Data
No full textOne desirable property of machine learning
algorithms is the ability to balance
the number of parameters in a model
in accordance with the amount of available data.
Incorporating nonparametric Bayesian priors into models is
one approach of automatically
adjusting model capacity to the amount of available data: with small
datasets, models are less complex
(require storing fewer parameters in memory), whereas with larger datasets, models
are implicitly more complex
(require storing more parameters in memory).
Thus, nonparametric Bayesian priors satisfy frequentist intuitions
about model complexity within a fully Bayesian framework.
This thesis presents several novel
machine learning models and applications that use
nonparametric Bayesian priors.
We introduce two novel models that use flat,
Dirichlet process priors. The first is an infinite mixture
of experts model, which builds
a fully generative, joint density model of the input and output space.
The second is a Bayesian
biclustering model, which simultaneously
organizes a
data matrix into
block-constant biclusters.
The model capable of efficiently processing very large, sparse matrices,
enabling cluster analysis on incomplete data matrices.
We introduce binary matrix factorization,
a novel matrix factorization model that, in contrast to
classic factorization methods, such as singular value decomposition,
decomposes a matrix using latent binary matrices.
We describe two nonparametric Bayesian priors
over tree structures. The first is an infinitely exchangeable
generalization of the nested
Chinese restaurant process that generates
data-vectors at a single node in the tree.
The second is a novel, finitely exchangeable
prior generates trees by first partitioning data indices into groups
and then by randomly
assigning groups to a tree.
We present two applications of the tree priors: the first
automatically learns probabilistic stick-figure models of motion-capture
data that recover
plausible structure and are robust to missing
marker data.
The second learns hierarchical
allocation models based on the latent Dirichlet allocation
topic model for document corpora,
where nodes in a topic-tree
are latent ``super-topics", and nodes
in a document-tree are latent
categories.
The thesis concludes
with a summary of contributions, a discussion
of the models and their limitations, and a brief outline
of potential future research
directions.Ph
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
Nonparametric Bayesian Methods for Extracting Structure from Data
Get PDFOne desirable property of machine learning
algorithms is the ability to balance
the number of parameters in a model
in accordance with the amount of available data.
Incorporating nonparametric Bayesian priors into models is
one approach of automatically
adjusting model capacity to the amount of available data: with small
datasets, models are less complex
(require storing fewer parameters in memory), whereas with larger datasets, models
are implicitly more complex
(require storing more parameters in memory).
Thus, nonparametric Bayesian priors satisfy frequentist intuitions
about model complexity within a fully Bayesian framework.
This thesis presents several novel
machine learning models and applications that use
nonparametric Bayesian priors.
We introduce two novel models that use flat,
Dirichlet process priors. The first is an infinite mixture
of experts model, which builds
a fully generative, joint density model of the input and output space.
The second is a Bayesian
biclustering model, which simultaneously
organizes a
data matrix into
block-constant biclusters.
The model capable of efficiently processing very large, sparse matrices,
enabling cluster analysis on incomplete data matrices.
We introduce binary matrix factorization,
a novel matrix factorization model that, in contrast to
classic factorization methods, such as singular value decomposition,
decomposes a matrix using latent binary matrices.
We describe two nonparametric Bayesian priors
over tree structures. The first is an infinitely exchangeable
generalization of the nested
Chinese restaurant process that generates
data-vectors at a single node in the tree.
The second is a novel, finitely exchangeable
prior generates trees by first partitioning data indices into groups
and then by randomly
assigning groups to a tree.
We present two applications of the tree priors: the first
automatically learns probabilistic stick-figure models of motion-capture
data that recover
plausible structure and are robust to missing
marker data.
The second learns hierarchical
allocation models based on the latent Dirichlet allocation
topic model for document corpora,
where nodes in a topic-tree
are latent ``super-topics", and nodes
in a document-tree are latent
categories.
The thesis concludes
with a summary of contributions, a discussion
of the models and their limitations, and a brief outline
of potential future research
directions.Ph
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
