117,364 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
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
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
Letter from unknown writer to Jesse L. Boyce
No full textLetter to Jesse L. Boyce from unknown author (possibly Jack) about the investigation into the powder magazine located in the Grand Canyon. Some personal news is included in the letter such as the writer's marriage to the daughter of C.A. Taylor, former Supervisor of Cochise County
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
Sarah L. Blum Author Visit - Warrior Nurse: PTSD and Healing
No full textHear Sarah L. Blum, author of Women Under Fire: Abuse in the Military, discuss her newest book, Warrior Nurse: PTSD and Healing followed by a Q&A and book signing.
Sarah L. Blum is a decorated Vietnam veteran who served as an operating room nurse during the intense fighting of 1967. In recognition of her service, she was awarded the Army Commendation Medal.
Sponsored by CWU Veterans Center and CWU Libraries.https://digitalcommons.cwu.edu/libraryevents/1252/thumbnail.jp
Lillian L. Lambert, Author, Speaker, and Entrepreneur
No full textLillian L. Lambert, Author, Speaker, and Entrepreneu
Letter to Alfred L. Shoemaker, February 10, 1948
Get PDFA handwritten letter from an unknown author addressed to Alfred L. Shoemaker, dated February 10, 1948. Within, the author discusses the Pennsylvania Dutch word for Ash Wednesday, along with traditions associated with this day.https://digitalcommons.ursinus.edu/shoemaker_documents/1118/thumbnail.jp
A machine learning approach to understand business processes
Get PDFBusiness processes (industries, administration, hospitals, etc.) become nowadays more and more complex and it is difficult to have a complete understanding of them. The goal of the thesis is to show that machine learning techniques can be used successfully for understanding a process on the basis of data, by means of clustering process related measures, induction of predictive models, and process discovery. This goal is achieved by means of two approaches: (i) classify process cases (e.g. patients) into logistic homogeneous groups and induce models that assign a new case to a logistic group and (ii) discover the underlying process. By doing so, the process can be modelled, analysed and improved. Another benefit is that systems can be designed more efficiently to support and control the processes more effectively. We target on the analysis of two sorts of data, namely aggregated data and sequence data. Aggregated data result from performing some transformations on raw data, focusing on a specific concept, that is not yet explicit in the raw data. This aggregation is similar to feature construction, as used in the machine learning domain. In this thesis, aggregated data are the variables that result from operationalizing the concept of process complexity. These aggregated data are used to develop logistic homogeneous clusters. This means that elements in different clusters will differ from the routing complexity point of view. We show that developing homogeneous clusters for a given process is relevant in connection with the induction of predictive models. Namely, the routing in the process can be predicted using the logistic clusters. We do not aim to provide concrete directives for building control systems, rather our models should be taken as indicatives of their potential. Sequence data describe the sequence of activities over time in a process execution. They are recorded in a process log, during the execution of the process steps. Due to exceptions, missing or incomplete registration and errors, the data can be noisy. By using sequence data, the goal is to derive a model explaining the events recorded. In situations without noise and sufficient information, we provide a method for building a process model from the process log. Moreover, we discuss the class of models for which it is possible to accurately rediscover the model by looking at the process log. Machine learning techniques are especially useful when discovering a process model from noisy sequence data. Such a model can be further analyzed and eventually improved, but these issues are beyond the scope of this thesis. Through the applications of our proposed methods on different data (e.g. hospital data, workflow data and administrative governmental data), we have shown that our methods result in useful models and subsequently can be used in practice. We applied our methods on data-sets for which (i) it was possible to aggregate relevant information and (ii) sequence data were available
On convex feasibility problems
No full textIn this paper we consider a projection method for convex feasibility problem that is known to converge only weakly. Exploiting a property concerning the intersection of a family of convex closed sets, we present a condition that makes them strongly convergent, without additional assumptions. AMS Subject Classification: Primary: (K5J15, 47N10, secondary 41a29, 47H05
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