1,720,953 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
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
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
High-dimensional Bayesian methods for interpretable nowcasting and risk estimation
Full text linkThis thesis presents new models for nowcasting and macro risk estimation using frontier Bayesian methods that enable incorporating Big Data into policy relevant prediction problems. We propose variable selection algorithms motivated from Bayesian
decision theory to make model outcomes interpretable to the policy maker.
In chapter 2, we propose a Bayesian Structural Time Series (BSTS) model for nowcasting GDP growth. This model jointly estimates latent time trends to capture
slow moving changes in economic conditions along-side a high dimensional mixed
frequency component that is extracted from higher frequency (monthly) cyclical information. We extend on previous implementations of the BSTS with priors and
variable selection methods which facilitate selection over latent time trends as well
as mixed-frequency information that remain tractable to the policy maker. Empirically, we provide a novel nowcast application where we use a large dimensional
set of Internet search terms to gain advance information about supply and demand
sentiment for the US economy before more commonly considered macro information
are available to the nowcaster. We find that our proposed BSTS model offers large
improvements over competing models and that Internet search terms matter for
nowcasts before hard information about the macro economy have been published.
A simulation exercise confirms the good performance of the proposed model.
Chapter 3 presents the T-SV-t-BMIDAS (Bayesian Mixed Data Sampling) model
for nowcasting quarterly GDP growth. The model incorporates a long-run time-varying trend (T) and t-distributed stochastic volatility accounting for outliers (SV-t) into a Bayesian multivariate MIDAS. To address the high-dimensionality of the
model, to account for group-correlation in mixed frequency data, and to make the
model interpretable to the policy maker, we propose a new combination of group-shrinkage prior with sparsification algorithm for variable selection. The prior flexibly
accommodates between-group sparsity and within-group correlation and allows to
communicate the joint importance of predictors over the data release cycle. We
evaluate the model for UK GDP growth nowcasts covering also the time-span of
the Covid-19 recession. The model is competitive prior to the pandemic relative to
various benchmark models, while yielding substantial nowcast improvements during
the pandemic. Contrary to many previous nowcasting approaches, the model reads
in sparse group signals from the data. Simulations show competitive performance
of the variable selection methodology, with particularly good performance to be
expected for highly correlated data as well as dense data-generating-processes.
Chapter 4 presents a new Bayesian Quantile Regression (BQR) model for high dimensional risk estimation. It extends the horseshoe prior to the BQR framework
and provides a fast sampling algorithm for computation that makes it efficient for
high-dimensional problems. A large scale simulation exercise reveals that compared
to alternative shrinkage priors, the proposed methods yield better performance in
coefficient bias and forecast error, especially in sparse data-generating processes
and in estimating extreme quantiles. In a high dimensional Growth-at-Risk forecasting application, we forecast tail risks as well as complete forecast densities using
a database covering over 200 variables related to the U.S. economy. Quantile specific and density calibration score functions show that the horseshoe prior provides
the best performance compared to competing Bayesian quantile regression priors,
especially at short and medium run horizons.
Bayesian quantile regression models with continuous shrinkage priors are known to
predict well but are hard to interpret due to lack of exact posterior sparsity. Chapter 5 bridges this gap by extending the idea of decoupling shrinkage and sparsity.
The proposed procedure follows two steps: First, the quantile regression posterior is
shrunk via state of the art continuous shrinkage priors; then, the posterior is sparsified by taking the Bayes optimal solution to maximising a policy maker’s utility
function with joint preference for predictive accuracy as well as sparsity. For the
sparsification component, we propose a new variant of the signal adaptive variable
selection algorithm that automates the choice of penalization in the integrated tility
through a quantile specific loss-function that works well in high dimensions. Large
scale simulations show that, compared to the un-sparsified regression posterior, the
selection procedure decreases coefficient bias irrespective of the true underlying degree of sparsity in the data, and goodness of variable selection is competitive with
traditional variable selection priors. A high dimensional Growth-at-Risk forecasting
application to the US shows that the method detects varying degrees of sparsity
across the conditional GDP distribution and that the sources to downside risk vary
substantially over time.
Inspired by the work of Giannone et al. (2021) on the “illusion of sparsity” from
sparse modelling techniques, this chapter (6) investigates whether the recently popularised global-local priors, firstly, are implicitly informative about sparsity and,
secondly, whether they are able to communicate the true degree of sparsity from
the data. We consider two methods of analysis: implicit model size distributions
and sparsification techniques which are tested on a host of economic data sets and
simulations. The findings motivate a new horseshoe type model to which we add a
prior that makes it a-priori agnostic about the degree of sparsity and is shown to be
competitive to the spike-and-slab of Giannone et al. (2021) for forecasting as well
as sparsity detection.
Chapter 7 concludes with summaries, limitations of the thesis, as well as directions
for future research
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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