Search (3 results, page 1 of 1)

  • × author_ss:"Small, H."
  • × theme_ss:"Informetrie"
  1. Boyack, K.W.; Small, H.; Klavans, R.: Improving the accuracy of co-citation clustering using full text (2013) 0.01 
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    Abstract
    Historically, co-citation models have been based only on bibliographic information. Full-text analysis offers the opportunity to significantly improve the quality of the signals upon which these co-citation models are based. In this work we study the effect of reference proximity on the accuracy of co-citation clusters. Using a corpus of 270,521 full text documents from 2007, we compare the results of traditional co-citation clustering using only the bibliographic information to results from co-citation clustering where proximity between reference pairs is factored into the pairwise relationships. We find that accounting for reference proximity from full text can increase the textual coherence (a measure of accuracy) of a co-citation cluster solution by up to 30% over the traditional approach based on bibliographic information.
  2. Small, H.: Update on science mapping : creating large document spaces (1997) 0.01 
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    Abstract
    Science mapping projects have been revived by the advent of virtual reality (VR) software capable of navigating large sysnthetic 3 dimensional spaces. Unlike the earlier mapping efforts aimed at creating simple maps at either a global or local level, the focus is now on creating large scale maps displaying many thousands of documents which can be input into the new VR systems. Presents a general framework for creating large scale document spaces as well as some new methods which perform some of the individual processing steps. The methods are designed primarily for citation data but could be applied to other types of data, including hypertext links
  3. Zitt, M.; Small, H.: Modifying the journal impact factor by fractional citation weighting : the audience factor (2008) 0.01 
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    Abstract
    A new approach to the field normalization of the classical journal impact factor is introduced. This approach, called the audience factor, takes into consideration the citing propensity of journals for a given cited journal, specifically, the mean number of references of each citing journal, and fractionally weights the citations from those citing journals. Hence, the audience factor is a variant of a fractional citation-counting scheme, but computed on the citing journal rather than the citing article or disciplinary level, and, in contrast to other cited-side normalization strategies, is focused on the behavior of the citing entities. A comparison with standard journal impact factors from Thomson Reuters shows a more diverse representation of fields within various quintiles of impact, significant movement in rankings for a number of individual journals, but nevertheless a high overall correlation with standard impact factors.

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