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Development of methodology and applied toolkit for assessment of uniformity of court practice (on the basis of analysis methods of “big data”)

Priority areas of development: law

Purpose of work: development of methodology of the automated assessment of uniformity of court practice within separate legal order.

The used methods:

In the design of mathematical toolkit to estimation of uniformity of court practice the following methods were used:

  • comparison of probability distributions. Methods of comparison of probabilistic distributions on limited selections1;
  • Cluster analysis (methods of classification and clustering of data within building of analytical multiple parameter systems)2.
  • OLAP (On-Line Analytical Processing) cubes3.

Within work on interpretation and verification of the obtained data the research collective relied on the modern legal theory, including modern legal positivism (G. Hart and adherents, see the bibliography), R. Dvorkin's theory4, the Critical Legal Studies direction.

Besides, traditional methods for law were used, among which:

  • classical legal-dogmatic method which is used, mainly, in the analysis of maintenance of national jurisprudence;
  • comparative method which is used when comparing national legal systems with one other.

Empirical base of research

official publications of normative legal acts, bases of normative legal acts and other legal information; program documents; Resolutions of Plenums of the Supreme Court of the Russian Federation and the Supreme Arbitrazh Court of the Russian Federation, the letter and the information letters of the Supreme Court of the Russian Federation and the Supreme Arbitrazh Court of the Russian Federation including generalizations and the reviews of court and arbitrazh practice published in official publications of courts of the Russian Federation or included in databases of legal information; the database of awards of arbitratzh courts for 2010-2017; results of consultations with experts; corpus of public sources on problems of legal regulation in a number of areas. In the research the empirical data described in the special literature used at implementation of the project were also taken into consideration.

Results of work

The study allowed receiving the following results:

a) in the field of the theory:

  • Mathematical apparatus of comparison of histograms offered to use in the analysis of histograms of distribution of characteristics of court practice is developed. In particular, in the section are formalized requirements of "universality" to methods of comparison of histograms. With application of the called requirements the most known cross-bin comparison methods are analyzed: Quadratic Form and Earth Movers Distance. As a result for EMD cross-bin coefficients are unambiguously defined. Besides, modification of the Square form which meets requirements of universality is described. The new linear cross-bin distance between histograms – expansion of a measure L_1 is also described. All offered methods of cross-bin comparison meet the formulated requirements of universality and are completely defined (with an accuracy of linear coefficient). The problem of comparison of characteristics of selections of two measured sizes in the absence of prior data on functions of distribution is solved by comparison of histograms.

б) in the field of methodology development:

  • methodology of assessment of uniformity of court practice by means of big data analysis, based on the analysis of exclusively measurable (quantitative) characteristics of affairs is developed. The essence of a method consists in comparison of distribution of separate characteristic in one "class" (selection of affairs for which there are no bases to assume differences in properties) with distribution of the same characteristic in other class. The main objective which is solved by the developed method which is based on the statistical analysis is a measurement of uniformity of distribution of characteristics of affairs of one category. The measured uniformity (in mathematical sense) is the evidence of uniform practice of proceedings. The developed method allows by application fixed (the general for all analysis and not depending on the researcher) formulas and ways to reveal signs of fulfillment/violation of uniformity of court practice by comparison of distributions of the measured characteristics.
  • analysis of the problems and opportunities connected with usage of big data anaysis in law is carried out;

в) in the field of obtaining new empirical knowledge:

  • within study approbation of the developed method is carried out and revealed actual degrees of uniformity (uniformity) of arbitrazh practice in a section of categories of cases (in official classification of Judicial department).

Extent of introduction, the recommendation about implementation or results of implementation of results of research

Results of study will find application in the following areas:

  • Russian and foreign applied studies in the field of assessment of court practice uniformity;
  • lawmaking work of the Government of the Russian Federation, including development of the system of measures for improvement of quality of rule-making;
  • sphere of practical work, connected with the analysis of court practice;
  • training of specialists in higher education institutions in the sphere of the law;
  • professional development of the experts (including public servants) facing need of participation in proceedings.

Public authorities, developers of regulations, teachers of educational institutions, scientists are among potential consumers of result.


[1] The review of a part from the used methods see: Porter F. Testing Consistency of Two Histograms, California Institute of Technology Lauritsen Laboratory for High Energy Physics, Pasadena, California, 2008.

[2] See https://en.wikipedia.org/wiki/Cluster_analysis. See, for example, “Stryukov R.K., Shashkin A.I. About modification of a method of the closest neighbors. VSU bulletin, Series: System analysis and information technologies, No. 1 2015”. Or Filipovych, Roman; Resnick, Susan M.; Davatzikos, Christos (2011). "Semi-supervised Cluster Analysis of Imaging Data". NeuroImage. 54 (3): 2185–2197. PMC 3008313. PMID 20933091. doi:10.1016/j.neuroimage.2010.09.074.

[3] https://en.wikipedia.org/wiki/OLAP_cube.

[4] Dworkin R. Taking Rights Seriously. 1978. Dworkin R. Empire of La. Cambridge, Mass., 1986.


Чураков В. Д. Big Data и юриспруденция: на одном ли мы пути?, in: Право и информация: вопросы теории и практики: Сборник материалов международной научно-практической конференции. Санкт-Петербург : Президентская библиотека имени Б.Н. Ельцина, 2018. С. 136-143. 
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Государственно-правовые основы ускоренного развития Дальнего Востока России.: Институт законодательства и сравнительного правоведения при Правительстве РФ, 2018. 
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