[Sigmetrics] Papers of Interest

[Peter Andrew Hook]

Dear Colleagues,


I would like to bring to your attention two papers:  (1) one is my own recently published in the online version of JASIST;  and (2) the second is a paper I saw yesterday on an unrelated listserv but cites frequently and has a direct relationship to this community.


Thank you to the many on this list that provided input and assistance as to my own paper which reflects the major findings of my doctoral dissertation.


Sincerely,


Peter Hook

http://slis.wayne.edu/faculty/bio.php?id=128096




(1) Using course-subject Co-occurrence (CSCO) to reveal the structure of an academic discipline: A framework to evaluate different inputs of a domain map. http://dx.doi.org/10.1002/asi.23630


This article proposes, exemplifies, and validates the use of course-subject co-occurrence (CSCO) data to generate topic maps of an academic discipline. A CSCO event is when 2 course-subjects are taught in the same academic year by the same teacher. A total of 61,856 CSCO events were extracted from the 2010-11 directory of the American Association of Law Schools and used to visualize the structure of law school education in the United States. Different normalization, ordination (layout), and clustering algorithms were compared and the best performing algorithm of each type was used to generate the final map. Validation studies demonstrate that CSCO produces topic maps that are consistent with expert opinion and 4 other indicators of the topical similarity of law school course-subjects. This research is the first to use CSCO to produce a visualization of a domain. It is also the first to use an expanded, multi-part gold standard to evaluate the validity of domain maps and the intermediate steps in their creation. It is suggested that the framework used herein may be adopted for other studies that compare different inputs of a domain map in order to empirically derive the best maps as measured against extrinsic sources of topical similarity (gold standards).



(2)  The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations.  http://arxiv.org/abs/1602.08409

Miguel R. Guevara, Dominik Hartmann, Manuel Aristarán, Marcelo Mendoza, César A. Hidalgo
(Submitted on 26 Feb 2016 (v1), last revised 29 Feb 2016 (this version, v2))

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps.

<http://arxiv.org/abs/1602.08409>

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