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Citation patterns in scientific revolutions


The method of classifying citations according to the context in the citing paper, previously developed by the authors, is applied to the study of scientific revolutions. In particular, the BCS theory of superconductivity ind the non-conservation of parity are investigated. The results can be easily interpreted in terms of the characteristic features of these discoveries. It is suggested that these two examples represent two different types of “paradigm” changes, thus prompting a considerable refinement of the usual dichotomous picture of “normal”vs. “breakthrough” science.

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  1. M. J. MORAVCSIK, Measures of Scientific Growth,Research Policy, 2 (1973) 266–275. M. J. MORAVCSIK, P. MURUGESAN, Function and Quality of Citation,Social Studies of Science, 5 (1975) 86–92. P. MURUGESAN, M. J. MORAVCSIK, Variation of the Nature of Citation Measures with Journals and Scientific Specialties,Journal of American Society for Information Science, 29 (1978) 141. The definitions in the Appendix of the present paper were reprintedverbatim by permission of J. Wiley & Sons, Inc. M. J. MORAVCSIK, P. MURUGESAN, E. SHEARER, An Analysis of Citation Patterns in Indian Physics,Science and Culture, 42 (1976) 295. M. J. MORAVCSIK, P. MURUGESAN, Some Results on the Classification of Citation Records of Individual Scientists, (unpublished). E. SHEARER, A Comparison of the Citation Patterns of “Big” and “Little” Science, Master's Thesis, University of Oregon, June 1976; E. SHEARER, M. J. MORAVCSIK, Citation Patterns in Little Science and Big Science, (to be published) (1976).

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  2. T. KUHN,The Structure of Scientific Revolutions, University of Chicago Press, 2nd ed., Chicago, 1970.

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  3. See for example E. L. McDOUGH,Social Studies of Science, 6 (1976) 51–76.

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  4. See e.g. J. COLE, S. COLE,Social Stratification in Science, University of Chicago Press, Chicago, 1972.

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  5. R. D. PARKS,Superconductivity, Marcel Dekker, Inc., New York, 1969.

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  6. The mathematically rigorous way of calculating the random error in such a situation would be through the formula\(\Delta p = \left[ {p(1 - p)} \right]^{\frac{1}{2}} N^{ - \frac{1}{2}} \) where p — the percentage in question in units of the total sample (e.g. 30% is 0.30), Δp — the error on this percentage (i.e. ±Δp again in units of the total sample, (e.g. ±5% appears as ±0.05), N—the total absolute size of the sample whose percentage we consider. Since p(1−p)<1/2 for any p [in fact p(1−p)≤1/4], the approximate formula we have been using for the error calculation turns out to be always overly conservative compared to the rigorous formula, which is just as well, since random errors might not be the only uncertainty attached to the percentages found in our work. We have been asked, from time to time, to provide also some other statistical measures for the “reliability” of our numbers, such as confidence limits, etc. We are reluctant to do so, since we feel that such “fancy” indicators might give a falsely optimistic and misleadingly “rigorous” impression about the extent to which such reliability can be determined. The approximate “errors” we have given in our papers provide an order of magnitude estimate of the uncertainties which is all one can, in good conscience, release.

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Moravcsik, M.J., Murugesan, P. Citation patterns in scientific revolutions. Scientometrics 1, 161–169 (1979).

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