Google Scholar Citations Working Paper

[Stephen J Bensman]

Our working paper--Power-law distributions, the h-index, and Google Scholar (GS) citations: a test of their relationship with economics Nobelists /  Stephen J. Bensman, Alice Daugherty, Lawrence J. Smolinsky, Daniel S. Sage, J. Sylvan Katz-has finally been posted on arXiv at the following URL:  http://arxiv.org/ftp/arxiv/papers/1411/1411.0928.pdf.  Its abstract is as follows:

"This paper comprises an analysis of whether Google Scholar (GS) can construct documentary sets relevant for the evaluation of the works of researchers. The researchers analyzed were two samples of Nobelists in economics: an original sample of five laureates downloaded in September, 2011; and a validating sample of laureates downloaded in October, 2013. Two methods were utilized to conduct this analysis. The first is distributional. Here it is shown that the distributions of the laureates' works by total GS citations belong within the Lotkaian or power-law domain, whose major characteristic is asymptote or "tail" to the right. It also proves that this asymptote is conterminous with the laureates' h-indexes, which demarcate their core oeuvre. This overlap is proof of both the ability of GS to form relevant documentary sets and the validity of the h-index. The second method is semantic. This method shows that the extreme outliers at the right tip of the tail-a signature feature of the economists' distributions-are not random events but related by subject to contributions to the discipline for which the laureates were awarded this prize. Another interesting finding is the important role played by working papers in the dissemination of new economic knowledge."

It is only a working paper, and it still needs to be revised and vetted.  One major thing that needs to be done is to incorporate the excellent work being done in Spain on academic search engines.  This paper validates and extends many of their ideas and findings.  However, as it stands, this paper does the following things: 1) proves that Webology should be based on the theory of Lotkaian informetrics advanced by Leo Egghe and Ronald Rousseau; 2) justifies the use of Anne-Wil Harzing's PoP program for profiling purposes; and 3) tests Stasa Milojević's ideas on the use of logarithmic binning to estimate parameters.

The key thing to understand is that Google Scholar forms relevant sets entirely by linkages without key words.  It had a subject classification system, which it dumped as unnecessary according to Ortega.  This paper shows why Google is successful in doing this by connecting its algorithm with probability theory, showing at what point linkages define semantics.  The basic theory behind this was initially developed by Eugene Garfield and further developed by Francis Narin  before being incorporated into the Google search engine by Larry Page.  If you are interested in why this is so, I suggest that you read the three top papers at the following URL:  http://arxiv.org/find/all/1/au:+Bensman_Stephen/0/1/0/all/0/1.

Respectfully,

Stephen J Bensman, Ph.D.
LSU Libraries
Lousiana State University
Baton Rouge, LA 70803
USA

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.asis.org/pipermail/sigmetrics/attachments/20141105/891b4082/attachment.html>