Abstract
In a series of two articles, I will show that the expected information content of distributions provides us with a straightforward means to develop a static and a dynamic model for the development of the sciences. In the first study, I analyze how knowledge about one indicator (nominal variable) can reduce our uncertainty in the prediction of other indicators, and how relations across various levels of aggregation can be assessed. In the second study, I will address the problem of the use of indicators and relations among them for predictions and reconstructions.
I will use the occurrences of words in texts as the prime nominal variable which can be easily counted by the machine. However, I will generalize the models for the multi-variate case, in which any indicator or nominal variable can be assessed in terms of its validity in relation to other indicators and its value for predictions.
Similar content being viewed by others
References
H. Theil,Statistical Decomposition Analysis, North-Holland, Amsterdam-London, 1972.
The informationh in bits received by a message in terms of the probabilityp that prevailed prior to the arrival of the message is equal to-log p. (C. H. Shannon, A Mathematical Theory of Communication,Bell System Technical Journal, 27 (1948) 379–423, and 623–656.)
F. Attneave,Applications of Information Theory to Psychology, Holt, Rinehart and Winston, New York, 1959.
L. Leydesdorff, Words and cowords as indicators of intellectual organization,Research Policy, 18 (1989) 209–223;O. Amsterdamska, L. Leydesdorff, Citations: Indicators of Significance?,Scientometrics, 15 (1989) 449–471. See also:L. Leydesdorff, O. Amsterdamska, “Dimensions of Citation Analysis,”Science, Technology and Human Values (forthcoming).
Searches were performed on December 8, 1988.
Leydesdorff, Amsterdamska, 1988.Op. cit., note 5. Searches were performed on December 8, 1988.
Searches were also performed on December 8, 1988.
Words occurring in subheadings, such as “Introduction”, “Methods,”, “Results,” and “Discussion”, were also excluded, since the subheadings are repeated in the STN-file in each paragraph belonging to that section.
H=H o+Q s H s, whichQ s is the weighing of the contribution of the entropy of each of the sections, and Ho is the between-sections entropy.
L. Leydesdorff. “In Search of Epistemic Networks”,Social Studies of Science (forthcoming)
Ibid. L. Leydesdorff. “In Search of Epistemic Networks,”Social Studies of Science (forthcoming)
See also:Theil, 1972., Chapter 3.
. pp. 157f.
K.E. Studer, D.E. Chubin,The Cancer Mision. Social Contexts of Biomedical Research, Sage, Beverly Hills, etc., 1980;M. Callon, J.-P. Courtial, W.A. Turner, S. Bauin, From translations to problematic networks: An introduction to co-word analysis,Social Science Information, 22 (1983) 191–235;L. Leydesdorff, The Development of frames of reference,Scientometrics, 9 (1986) 103–125;W. Shrum, N. Mullins, Network Analysis in the Study of Science and Technology, in:A.F.J. van Raan (Ed.),Handbook of Quantitative Studies of Science and Technology, Amsterdam: Elsevier, 1988.
W.S. Robinson, Ecological correlations and the behavior of individuals,American Sociological Review, 15 (1950) 351–357.
See, for example:L.I. Langbein, A.J. Lichtman,Ecological Inference, Quantitative Application in the Social Sciences Nr. 07-010, Sage, Beverly Hills, etc., 1979;P. Van den Eeden, H.J.M. Huttner,Multilevel Research, Sage, Beverly Hills, etc., 1982.
A weighted average is appropriate since the entropy is additive. (Theil, 1972.. p. 18.)
. p. 66.
See also:G. Salton, M.J. McGill,Introduction to Modern Information Retrieval, McGraw-Hill, Auckland, etc., 1983, 63ff.
M. Callon, J. Law, A. Rip (Eds.),Mapping the Dynamics of Science and Technology, MacMillan, London, 1986.
D. Mowery, N. Rosenberg, The influence of market demand upon innovation: a critical review of some recent empirical studies,Research Policy, 8 (1979) 102–153.
L. Leydesdorff, The relations bBetween qualitative theory and scientometric methods in S&T-studies. Introduction to the Topical Issue,Scientometrics, 15 (1989), 333–347.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Leydesdorff, L. Relations among science indicators or more generally among anything one might wish to count about texts. Scientometrics 18, 281–307 (1990). https://doi.org/10.1007/BF02017766
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF02017766