130,354 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
The construction of Karen Karnak: The multi-author-function
No full textThis thesis is situated within the comparatively recent developments of Web 2.0 and the emergence of interactive WikiMedia, and explores the mode of authorship within a Read/Write culture compared to that of a Read/Only tradition. The hypothesis of this study is that the role of the audience has become merged with the author, and as such, represents new functions and attributes, distinct from a more conventional concept of authorship, in which the roles of audience and author are more separate. Read/Write and participatory culture, as defined by this study, is focused on collaboration, and includes the influences of D.I.Y. culture, Open-Source practices and the production of text by multiple authors. Multi-authorship presents a re-thinking of several concepts which support the notion of the individual author, since the focus of multi-authorship is not on attribution and ownership of a finished text, but on the continued malleability of a text. Modes of multi-authorship, demonstrated in the use of the pseudonyms Alan Smithee and Karen Eliot, represent declarative authors whose names signify multiple origins, whilst concurrently indicating a distinct body of work. The function of these names form an important context to this study, since primary research involves the construction of an experimental mode of multi-authorship utilising WikiMedia technology and the interaction of thirty nine participants, who are invited to create a body of work under the collective pseudonym Karen Karnak. The data generated by this experiment is analysed using aspects of Michel Foucault's author-function to identify and determine power structures inherent in the WikiMedia context. The interplay of power structures, including concepts such as identity, ownership and the body of work, affect the resulting mode of authorship and contribute to the construction of Karen Karnak, suggesting further areas of research into the emerging multi-author
Author Co-Citation Analysis (ACA): a powerful tool for representing implicit knowledge of scholar knowledge workers
No full textIn the last decade, knowledge has emerged as one of the most important and valuable organizational assets. Gradually this importance caused to emergence of new discipline entitled ―knowledge management‖. However one of the major challenges of knowledge management is conversion implicit or tacit knowledge to explicit knowledge. Thus Making knowledge visible so that it can be better accessed, discussed, valued or generally managed is a long-standing objective in knowledge management. Accordingly in this paper author co- citation analysis (ACA) will be proposed as an efficient technique of knowledge visualization in academia (Scholar knowledge workers)
Open access self-archiving: An author study
No full textThis, our second author international, cross-disciplinary study on open access had 1296 respondents. Its focus was on self-archiving. Almost half (49%) of the respondent population have self-archived at least one article during the last three years. Use of institutional repositories for this purpose has doubled and usage has increased by almost 60% for subject-based repositories. Self-archiving activity is greatest amongst those who publish the largest number of papers. There is still a substantial proportion of authors unaware of the possibility of providing open access to their work by self-archiving. Of the authors who have not yet self-archived any articles, 71% remain unaware of the option. With 49% of the author population having self-archived in some way, this means that 36% of the total author population (71% of the remaining 51%), has not yet been appraised of this way of providing open access. Authors have frequently expressed reluctance to self-archive because of the perceived time required and possible technical difficulties in carrying out this activity, yet findings here show that only 20% of authors found some degree of difficulty with the first act of depositing an article in a repository, and that this dropped to 9% for subsequent deposits. Another author worry is about infringing agreed copyright agreements with publishers, yet only 10% of authors currently know of the SHERPA/RoMEO list of publisher permissions policies with respect to self-archiving, where clear guidance as to what a publisher permits is provided. Where it is not known if permission is required, however, authors are not seeking it and are self-archiving without it. Communicating their results to peers remains the primary reason for scholars publishing their work; in other words,
researchers publish to have an impact on their field. The vast majority of authors (81%) would willingly comply with a mandate from their employer or research funder to deposit copies of their articles in an institutional or subject-based repository. A further 13% would comply reluctantly; 5% would not comply with such a mandate
Also By The Same Author: AKTiveAuthor, a Citation Graph Approach to Name Disambiguation
No full textThe desire for definitive data and the semantic web drive for inference over heterogeneous data sources requires co-reference resolution to be performed on those data. In particular, name disambiguation is required to allow accurate publication lists, citation counts and impact measures to be determined. This paper describes a graph-based approach to author disambiguation on large-scale citation networks. Using self-citation, co-authorship and document source analyses, AKTiveAuthor clusters papers, achieving precision of 0.997 and recall of 0.818 over a test group of eight surname clusters
Author Name Disambiguation using Large Language Models: Contributions to a system for open reproducible publication research
No full textAuthor name disambiguation, otherwise described as (publication) record linking, is a problem that has had considerable research dedicated to its solv- ing. Author attributions, calculating research met- rics and conducting literature reviews are amongst processes that experience increased difficulty due to ambiguous author names. In this study, a novel approach is presented to disambiguate au- thors related to scientific publications, using Large Language Models (LLMs) in combination with the Alexandria3k software package. LLMs have shown great potential in processing, analysing and drawing conclusions when presented with human- readable data. The approach presented in this study supplies a LLM with known attributes of publica- tion records and authors, such as names, affiliations and co-authors, to determine whether records writ- ten by authors with ambiguous names can be linked to the same real-world person. Using Alexan- dria3k, a dataset of authors and publications with confirmed identities is created to test and validate the approach. Finally, the approach is measured against state-of-the-art methods to disambiguate author names and different configurations are pre- sented and discussed.CSE3000 Research ProjectComputer Science and Engineerin
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
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