Search (23 results, page 2 of 2)

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Abstract

In their 1998 article "Visualizing a discipline: An author cocitation analysis of information science, 1972-1995," White and McCain used multidimensional scaling, hierarchical clustering, and factor analysis to display the specialty groupings of 120 highly-cited ("paradigmatic") information scientists. These statistical techniques are traditional in author cocitation analysis (ACA). It is shown here that a newer technique, Pathfinder Networks (PFNETs), has considerable advantages for ACA. In PFNETs, nodes represent authors, and explicit links represent weighted paths between nodes, the weights in this case being cocitation counts. The links can be drawn to exclude all but the single highest counts for author pairs, which reduces a network of authors to only the most salient relationships. When these are mapped, dominant authors can be defined as those with relatively many links to other authors (i.e., high degree centrality). Links between authors and dominant authors define specialties, and links between dominant authors connect specialties into a discipline. Maps are made with one rather than several computer routines and in one rather than many computer passes. Also, PFNETs can, and should, be generated from matrices of raw counts rather than Pearson correlations, which removes a computational step associated with traditional ACA. White and McCain's raw data from 1998 are remapped as a PFNET. It is shown that the specialty groupings correspond closely to those seen in the factor analysis of the 1998 article. Because PFNETs are fast to compute, they are used in AuthorLink, a new Web-based system that creates live interfaces for cocited author retrieval an the fly.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.5, S.423-434

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Abstract

Many authors have posited a social component in citation, the consensus being that the citers and citees often have interpersonal as well as intellectual ties. Evidence for this belief has been rather meager, however, in part because social networks researchers have lacked bibliometric data (e.g., pairwise citation counts from online databases), and citation analysts have lacked sociometric data (e.g., pairwise measures of acquaintanceship). In 1997 Nazer extensively measured personal relationships and communication behaviors in what we call "Globenet," an international group of 16 researchers from seven disciplines that was established in 1993 to study human development. Since Globenet's membership is known, it was possible during 2002 to obtain citation records for all members in databases of the Institute for Scientific Information. This permitted examination of how members cited each other (intercited) in journal articles over the past three decades and in a 1999 book to which they all contributed. It was also possible to explore links between the intercitation data and the social and communication data. Using network-analytic techniques, we look at the growth of intercitation over time, the extent to which it follows disciplinary or interdisciplinary lines, whether it covaries with degrees of acquaintanceship, whether it reflects Globenet's organizational structure, whether it is associated with particular in-group communication patterns, and whether it is related to the cocitation of Globenet members. Results show cocitation to be a powerful predictor of intercitation in the journal articles, while being an editor or co-author is an important predictor in the book. Intellectual ties based an shared content did better as predictors than content-neutral social ties like friendship. However, interciters in Globenet communicated more than did noninterciters.

Source

Journal of the American Society for Information Science and technology. 55(2004) no.2, S.111-126

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Abstract

This study systematically compares retrievals on 11 topics across five well-known data bases, with MEDLINE's subject indexing as a focus. Each topic was posed by a researcher in the medical behavioral sciences. Each was searches in MEDLINE, EXCERPTA MEDICA, and PSYCHINFO, which permit descriptor searches, and in SCISEARCH and SOCIAL SCISEARCH, which express topics through cited references. Searches on each topic were made with (1) descriptors, (2) cited references, and (3) natural language (a capabiblity common to all five data bases). The researchers who posed the topics judged the results. In every case, the set of records judged relevant was used to to calculate recall, precision, and novelty ratios. Overall, MEDLINE had the highest recall percentage (37%), followed by SSCI (31%). All searches resulted in high precision ratios; novelty ratios of data bases and searches varied widely. Differences in record format among data bases affected the success of the natural language retrievals. Some 445 documents judged relevant were not retrieved from MEDLINE using its descriptors; they were found in MEDLINE through natural language or in an alternative data base. An analysis was performed to examine possible faults in MEDLINE subject indexing as the reason for their nonretrieval. However, no patterns of indexing failure could be seen in those documents subsequently found in MEDLINE through known-item searches. Documents not found in MEDLINE primarily represent failures of coverage - articles were from nonindexed or selectively indexed journals