Skip to main navigation menu Skip to main content Skip to site footer
##common.pageHeaderLogo.altText##

Articles

Vol. 11 (2024): Revista de Estudos Empíricos em Direito

QUANTITATIVE RESEARCH IN LAW: A BIBLIOGRAPHIC REVIEW

DOI
https://doi.org/10.19092/reed.v11.830
Submitted
April 27, 2023
Published
2025-01-13

Abstract

Traditionally, there is a predominance of qualitative research over quantitative research in academic studies in the legal field. However, the use of quantitative methods in law (statistical analysis, modeling, simulation, among others) has become an area on the rise in the legal field, seeking to understand the legal impacts in a more precise and objective way. In this sense, the main objective of this article is to discuss quantitative research in Law, in addition to addressing some empirical works carried out with legislation, jurisprudence and patents. Methodologically, this is a descriptive study, in which the inductive method was used, based on the idea that legal research tends to be mostly qualitative and that the expansion of such research in Law can enable a more comprehensive understanding. of the object of study. This work is justified insofar as it is necessary to quantitatively explore the concrete application of the law. In this sense, the dialogue between theory and data adds up, as while some studies prioritize hypothesis testing, others aim at elucidating specific observations and developing new hypotheses.

References

  1. Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., & Lampos, V. (2016). Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective. PeerJ Computer Science, 2, e93. https://peerj.com/articles/cs-93.pdf
  2. Almuslim, I., & Inkpen, D. (2022, March). Legal Judgment Prediction for Canadian Appeal Cases. In 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA) (pp. 163-168). https://doi.org/10.1109/CDMA54072.2022.00032
  3. Anand, D., & Wagh, R. (2022). Effective deep learning approaches for summarization of legal texts. Journal of King Saud University-Computer and Information Sciences, 34(5), 2141-2150. https://doi.org/10.1016/j.jksuci.2019.11.015
  4. Aranha, C., & Passos, E. (2006). A tecnologia de mineração de textos. Revista Eletrônica de Sistemas de Informação, 5(2). https://doi.org/10.21529/RESI.2006.0502001
  5. Aureli, S. (2017). A comparison of content analysis usage and text mining in CSR corporate disclosure. International Journal of Digital Accounting Research, 17. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=2c4e6aff81a835779415af37551b12c3b7a50f19
  6. Barros, R., Lorenzi, F. & Wives, L. K. (2018). Recent trends and future technology in applied intelligence, lecture notes in computer science. Cham: Springer International Publishing. 10868
  7. Bayrak, T. (2020). A content analysis of top-ranked universities’ mission statements from five global regions. International Journal of Educational Development, 72, 102130. https://doi.org/10.1016/j.ijedudev.2019.102130
  8. Been, J. V., Mackay, D. F., Millett, C., Pell, J. P., Van Schayck, O. C., & Sheikh, A. (2015). Impact of smoke-free legislation on perinatal and infant mortality: a national quasi-experimental study. Scientific reports, 5(1), 1-12. https://link.springer.com/content/pdf/10.1038/srep13020.pdf
  9. Bernoulli, N. (1709). Dissertatio inauguralis mathematico-juridica de usu artis conjectandi in jure. Typis Johannis Conradi à Mechel.
  10. Bertalan, V. G. F., & Ruiz, E. E. S. (2020, March). Predicting Judicial Outcomes in the Brazilian Legal System Using Textual Features. In DHandNLP@ PROPOR (pp. 22-32). https://ceur-ws.org/Vol-2607/paper3.pdf
  11. Bobbio, N. (2003). Teoria da norma jurídica. Edipro.
  12. Buchanan, J., Chai, D. H., & Deakin, S. (2014). Empirical analysis of legal institutions and institutional change: multiple-methods approaches and their application to corporate governance research. Journal of Institutional Economics, 10(1), 1-20. https://doi.org/10.1017/S1744137413000349
  13. Burris, S., Wagenaar, A. C., Swanson, J., Ibrahim, J. K., Wood, J., & Mello, M. M. (2010). Making the case for laws that improve health: a framework for public health law research. The milbank quarterly, 88(2), 169-210. https://doi.org/10.1111/j.1468-0009.2010.00595.x
  14. Capra, F., & Mattei, U. (2018). A revolução ecojurídica: o direito sistêmico em sintonia com a natureza e a comunidade. Trad. de Jeferson Luiz Camargo. São Paulo: Curtix.
  15. Cardellino, C., Teruel, M., Alemany, L. A., & Villata, S. (2017). Legal NERC with ontologies, Wikipedia and curriculum learning. In 15th European Chapter of the Association for Computational Linguistics (EACL 2017) (pp. 254-259). https://dx.doi.org/10.18653/v1/E17-2041
  16. Chalkidis, I., Androutsopoulos, I., & Aletras, N. (2019). Neural legal judgment prediction in English. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 28 Jul - 02 Aug 2019, Florence, Italy. ACL , pp. 4317-4323. https://core.ac.uk/download/pdf/223214543.pdf
  17. Chalkidis, I., Androutsopoulos, I., & Michos, A. (2017). Extracting contract elements. In Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law (pp. 19-28). https://doi.org/10.1145/3086512.3086515
  18. Chalkidis, I., Androutsopoulos, I., & Michos, A. (2018). Obligation and prohibition extraction using hierarchical RNNs. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 254–259. https://doi.org/10.48550/arXiv.1805.03871
  19. Coupette, C. (2019). Juristische netzwerkforschung: modellierung, quantifizierung und visualisierung relationaler daten im recht. Mohr Siebeck. https://doi.org/10.1628/978-3-16-157012-4
  20. Couto, M. B., & de Oliveira, S. P. (2016). Gestão da justiça e do conhecimento: a contribuição da jurimetria para a administração da justiça. Revista Jurídica, 2(43), 771-801. http://dx.doi.org/10.26668/revistajur.2316-753X.v2i43.1869
  21. Cross, F. B., Smith, T. A., & Tomarchio, A. (2007). The reagan revolution in the network of law. Emory LJ, 57, 1227. https://dx.doi.org/10.2139/ssrn.909217
  22. de Andrade, M. D. (2018). A utilização do sistema R-studio e da jurimetria como ferramentas complementares à pesquisa jurídica. Revista Quaestio Iuris, 11(2), 680-692. https://doi.org/10.12957/rqi.2018.29221
  23. De Fortuny, E. J., De Smedt, T., Martens, D., & Daelemans, W. (2014). Evaluating and understanding text-based stock price prediction models. Information Processing & Management, 50(2), 426-441. http://dx.doi.org/10.1016/j.ipm.2013.12.002
  24. de Moraes, A. R. A., & Demercian, P. (2017). Um novo modelo de atuação criminal para o Ministério Público brasileiro: agências e laboratório de jurimetria. Revista Jurídica da Escola Superior do Ministério Público de São Paulo, 11(1).
  25. Dragoni, M., Villata, S., Rizzi, W., & Governatori, G. (2016, December). Combining NLP approaches for rule extraction from legal documents. In 1st Workshop on MIning and REasoning with Legal texts. https://hal.science/hal-01572443
  26. Epstein, L., & Martin, A. D. (2014). An introduction to empirical legal research. Oxford University Press.
  27. Fjelstul, J. C. (2019). The evolution of European Union law: A new data set on the Acquis Communautaire. European Union Politics, 20(4), 670-691. https://doi.org/10.1177/1465116519842947
  28. Forji, A. G. (2010). The correlation between law and behaviour as pillars of human society. International Journal of Punishment and Sentencing, The, 6(3), 85-98. https://core.ac.uk/download/33737735.pdf
  29. Frankenreiter, J., & Livermore, M. A. (2020). Computational methods in legal analysis. Annual Review of Law and Social Science, 16, 39-57. https://doi.org/10.1146/annurev-lawsocsci-052720-121843
  30. Goanta, C. (2017). Big law, big data. Law and Method, Forthcoming. https://ssrn.com/abstract=3166290
  31. Gostin, L. O., Burris, S., & Lazzarini, Z. (1999). The law and the public's health: a study of infectious disease law in the United States. Colum. L. Rev., 99, 59-128. https://pubmed.ncbi.nlm.nih.gov/10558403/
  32. Gschwend, T., & Schimmelfennig, F. (2011). Research design in political science: How to practice what they preach. British Library.
  33. Hald, A. (1998). A History of Mathematical Statistics from 1750 to 1930. Wiley-Interscience.
  34. Haney, B. (2020). Applied Natural Language Processing for Law Practice. B.C. Intell. Prop. & Tech. F. https://dx.doi.org/10.2139/ssrn.3476351
  35. Hart, H. L. A., Hart, H. L. A., Raz, J., & Green, L. (2012). The concept of law. Oxford University Press.
  36. Heise, M. (2011). An Empirical Analysis of Empirical Legal Scholarship Production, 1990-2009. U. Ill. L. Rev., 1739. http://scholarship.law.cornell.edu/facpub/700?utm_source=scholarship.law.cornell.edu%2Ffacpub%2F700&utm_medium=PDF&utm_campaign=PDFCoverPages
  37. Ho, D. E., & Kramer, L. (2013). Introduction: the empirical revolution in law. Stanford Law Review, 1195-1202. https://www.jstor.org/stable/23528249
  38. Ikram, A. Y., & Chakir, L. (2019). Arabic text classification in the legal domain. In 2019 3rd international conference on intelligent computing in data sciences (pp. 1–6). http://dx.doi.org/10.1109/ICDS47004.2019.8942343
  39. Instituto de Pesquisa Econômica Aplicada – IPEA. (2006). O que é? Desvio padrão. Desafios do Desenvolvimento, 88, 13. https://www.ipea.gov.br/desafios/index.php?option=com_content&view=article&id=2104:catid=28&Itemid=2
  40. Jacob de Menezes-Neto, E., & Clementino, M. B. M. (2022). Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts. PloS one, 17(7), e0272287. https://doi: 10.1371/journal.pone.0272287
  41. Ji, D., Tao, P., Fei, H., & Ren, Y. (2020). An end-to-end joint model for evidence information extraction from court record document. Information Processing & Management, 57(6), 102305. http://dx.doi.org/10.1016/j.ipm.2020.102305
  42. Judd, S. (2010). The MBR book: principles and applications of membrane bioreactors for water and wastewater treatment. Elsevier.
  43. Jun, S., & Sung Park, S. (2013). Examining technological innovation of Apple using patent analysis. Industrial Management & Data Systems, 113(6), 890-907. https://doi.org/10.1108/IMDS-01-2013-0032
  44. Juventia, S. D., Jones, S. K., Laporte, M. A., Remans, R., Villani, C., & Estrada-Carmona, N. (2020). Text mining national commitments towards agrobiodiversity conservation and use. Sustainability, 12(2), 715. https://doi.org/10.3390/su12020715
  45. Katz, D. M., Bommarito II, M. J., & Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PLoS One, 12(4), 1–18. http://dx.doi.org/10.1371/journal.pone.0174698
  46. Katz, D. M., Bommarito, M. J., & Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PloS one, 12(4), e0174698. https://doi.org/10.1371/journal.pone.0174698
  47. Katz, D. M., Coupette, C., Beckedorf, J., & Hartung, D. (2020). Complex societies and the growth of the law. Scientific reports, 10(1), 18737. https://doi.org/10.1038/s41598-020-73623-x
  48. Katz, D. M., Hartung, D., Gerlach, L., Jana, A., & Bommarito II, M. J. (2023). Natural Language Processing in the Legal Domain. https://doi.org/10.48550/arXiv.2302.12039
  49. Kelsen, H. (1999). Teoria Pura do Direito. Livraria Martins Fontes.
  50. Kowsrihawat, K., Vateekul, P., & Boonkwan, P. (2018). Predicting judicial decisions of criminal cases from Thai Supreme Court using bi-directional GRU with attention mechanism. In 2018 5th Asian conference on defense technology (pp. 50–55). http://dx.doi.org/10.1109/ACDT.2018.8592948
  51. Kowsrihawat, K., Vateekul, P., & Boonkwan, P. (2018). Predicting judicial decisions of criminal cases from Thai Supreme Court using bi-directional GRU with attention mechanism. In 2018 5th Asian conference on defense technology (pp. 50–55). http://dx.doi.org/10.1109/ACDT.2018.8592948
  52. Lage-Freitas, A., Allende-Cid, H., Santana, O., & Oliveira-Lage, L. (2022). Predicting Brazilian court decisions. PeerJ Computer Science, 8, e904. https://doi.org/10.7717/peerj-cs.904
  53. Li, X., Wu, P., & Wang, W. (2020). Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong. Information Processing & Management, 57(5), Article 102212. http://dx.doi.org/10.1016/j.ipm.2020.102212
  54. Livermore, M., & Rockmore, D. (2019). Law as Data: Computation. Text, and the Future of Legal Analysis. Santa Fe Institute Press.
  55. Lodi, G. (2017). O impacto da Legislação e das Instituições Judiciárias no desenvolvimento econômico e social do Brasil. A Economia em Revista-AERE, 25(1), 109-129. https://doi.org/10.4025/aere.v25i1.36947
  56. Loevinger, L. (1948). Jurimetrics: The Next Step Forward. Minn. L. Rev., 33, 455. https://scholarship.law.umn.edu/mlr/1796
  57. Loevinger, L. (1963). Jurimetrics: The methodology of legal inquiry. Law and contemporary problems, 28(1), 5-35. https://doi.org/10.2307/1190721
  58. Long, S., Tu, C., Liu, Z., & Sun, M. (2019). Automatic judgment prediction via legal reading comprehension. In M. Sun, X. Huang, H. Ji, Z. Liu, & Y. Liu (Eds.), Chinese computational linguistics (pp. 558–572). Cham: Springer International Publishing, http://dx.doi.org/10.1007/978-3-030-32381-3_45
  59. Malta, D. C., Silva, M. M. A. D., Lima, C. M. D., Soares Filho, A. M., Montenegro, M. D. M. S., Mascarenhas, M. D. M., Morais Neto, L. O. de, Temporão, J. G. & Penna, G. O. (2010). Impacto da legislação restritiva do álcool na morbimortalidade por acidentes de transporte terrestre-Brasil, 2008. Epidemiologia e Serviços de Saúde, 19(1), 78-78. http://dx.doi.org/10.5123/S1679-49742010000100009
  60. Mattar, F. N. (2001). Pesquisa de marketing. Atlas.
  61. Medvedeva, M., Vols, M., & Wieling, M. (2020). Using machine learning to predict decisions of the European Court of Human Rights. Artificial Intelligence and Law, 28, 237-266. https://doi.org/10.1007/s10506-019-09255-y
  62. Medvedeva, M., Wieling, M., & Vols, M. (2023). Rethinking the field of automatic prediction of court decisions. Artificial Intelligence and Law, 31(1), 195-212. https://doi.org/10.1007/s10506-021-09306-3
  63. Menezes, D., & Barros, G. P. (2017). Breve análise sobre a jurimetria, os desafios para a sua implementação e as vantagens correspondentes. Duc In Altum-Cadernos de Direito, 9(19).
  64. Mumcuoğlu, E., Öztürk, C. E., Ozaktas, H. M., & Koç, A. (2021). Natural language processing in law: Prediction of outcomes in the higher courts of Turkey. Information Processing & Management, 58(5), 102684. https://doi.org/10.1016/j.ipm.2021.102684
  65. Nallapati, R., & Manning, C. D. (2008). Legal docket classification: Where machine learning stumbles. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 438-446). https://aclanthology.org/D08-1046.pdf
  66. Niklaus, J., Chalkidis, I., & Stürmer, M. (2021). Swiss-judgment-prediction: A multilingual legal judgment prediction benchmark. https://doi.org/10.48550/arXiv.2110.00806
  67. Nunes, M. G. (2012). Jurimetria aplicada ao direito societário: um estudo estatístico da dissolução de sociedade no Brasil (Doctoral dissertation, Pontifícia Universidade Católica de São Paulo).
  68. Nunes, M. G. (2016). Jurimetria: como a estatística pode reinventar o direito. Revista dos Tribunais.
  69. Nunez‐Mir, G. C., Iannone III, B. V., Pijanowski, B. C., Kong, N., & Fei, S. (2016). Automated content analysis: addressing the big literature challenge in ecology and evolution. Methods in Ecology and Evolution, 7(11), 1262-1272. https://doi.org/10.1111/2041-210X.12602
  70. Oxford Languages. (n.d.). Conceito de Direito. https://languages.oup.com/
  71. Priesnitz, M. C., Camargo, M. E., Fabris, J. P., & Russo, S. L. (2017). Depósitos de patentes pelas universidades federais brasileiras: distribuição regional e o impacto da lei da inovação. Suzana Leitão Russo; Maria Rita de Morais Chaves Santos.(Org.). Propriedade Intelectual, Tecnologias e Empreendedorismo. 1ed. Aracaju: Associação Acadêmica de Propriedade Intelectual, 1, 15-21.
  72. Processing & Management, 56(6), Article 102086. http://dx.doi.org/10.1016/j.ipm.2019.102086
  73. Qian, Y., Deng, X., Ye, Q., Ma, B., & Yuan, H. (2019). On detecting business event from the headlines and leads of massive online news articles. Information
  74. Ramírez, R. A., Díaz, Y. V., & Fernández, Y. A. (2016). Jurimetría: una opción para la sociedad. Serie Científica de la Universidad de las Ciencias Informáticas, 9(4), 1-10.
  75. Richardson, R. J. (1999). Pesquisa Social: Métodos e técnicas. São Paulo: Editora Atlas SA.
  76. Rockmore, D. N., Fang, C., Foti, N. J., Ginsburg, T., & Krakauer, D. C. (2018). The cultural evolution of national constitutions. Journal of the Association for Information Science and Technology, 69(3), 483-494. https://doi.org/10.1002/asi.23971
  77. Ruger, T. W., Kim, P. T., Martin, A. D., & Quinn, K. M. (2004). The supreme court forecasting project: Legal and political science approaches to predicting supreme court decisionmaking. Columbia Law Review, 1150-1210. https://doi.org/10.2307/4099370
  78. Ruhl, J. B., Katz, D. M., & Bommarito, M. J. (2017). Harnessing legal complexity. Science, 355(6332), 1377-1378.
  79. Rutherford, A., Lupu, Y., Cebrian, M., Rahwan, I., LeVeck, B. L., & Garcia-Herranz, M. (2018). Inferring mechanisms for global constitutional progress. Nature Human Behaviour, 2(8), 592-599. https://doi.org/10.1038/s41562-018-0382-8
  80. Schumaker, R. P., & Chen, H. (2009). A quantitative stock prediction system based on financial news. Information Processing & Management, 45(5), 571–583. http://dx.doi.org/10.1016/j.ipm.2009.05.001
  81. Stigler, S. M. (1986). The history of statistics: The measurement of uncertainty before 1900. Harvard University Press.
  82. Strickson, B., & De La Iglesia, B. (2020). Legal judgement prediction for uk courts. In Proceedings of the 3rd International Conference on Information Science and Systems (pp. 204-209). https://doi.org/10.1145/3388176.3388183
  83. Şulea, O. M., Zampieri, M., Malmasi, S., Vela, M., Dinu, L. P., & Van Genabith, J. (2017). Exploring the use of text classification in the legal domain. https://doi.org/10.48550/arXiv.1710.09306
  84. Şulea, O.-M., Zampieri, M., Vela, M., & van Genabith, J. (2017). Predicting the law area and decisions of french supreme court cases. In Proceedings of the international conference recent advances in natural language processing (pp. 716–722). Varna, Bulgaria: INCOMA Ltd., http://dx.doi.org/10.26615/978-954-452-049-6_092
  85. Tamames, J., & de Lorenzo, V. (2010). EnvMine: A text-mining system for the automatic extraction of contextual information. BMC bioinformatics, 11(1), 1-10. https://doi.org/10.1186/1471-2105-11-294
  86. Tobback, E., Naudts, H., Daelemans, W., de Fortuny, E. J., & Martens, D. (2018). Belgian economic policy uncertainty index: Improvement through text mining. International Journal of Forecasting, 34(2), 355-365. https://doi.org/10.1016/j.ijforecast.2016.08.006
  87. Todorov, J. C. (2005). Laws and the complex control of behavior. Behavior and Social Issues, 14(2), 86-91.
  88. Truyens, M., & Van Eecke, P. (2014). Legal aspects of text mining. Computer law & security review, 30(2), 153-170. https://doi.org/10.1016/j.clsr.2014.01.009
  89. Tuke, J., Nguyen, A., Nasim, M., Mellor, D., Wickramasinghe, A., Bean, N., & Mitchell, L. (2020). Pachinko prediction: A Bayesian method for event prediction from social media data. Information Processing & Management, 57(2), Article 102147. http://dx.doi.org/10.1016/j.ipm.2019.102147
  90. Tyler, T. R., & Darley, J. M. (1999). Building a law-abiding society: Taking public views about morality and the legitimacy of legal authorities into account when formulating substantive law. Hofstra L. Rev., 28, 707.
  91. Vijayarani, S., Ilamathi, M. J., & Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
  92. Virtucio, M. B. L., Aborot, J. A., Abonita, J. K. C., Aviñante, R. S., Copino, R. J. B., Neverida, M. P., Osiana, V. O., Peramo, E. C., Syjuco, J. G., & Tan, G. B. A. (2018). Predicting decisions of the Philippine supreme court using natural language processing and machine learning. In 2018 IEEE 42nd annual computer software and applications conference (pp. 130–135). http://dx.doi.org/10.1109/COMPSAC.2018.10348
  93. Visser, J. (2006). Jurimetrics, safety and security. In International Review of Law, Computers and Technology, vol. 20, n. 1/2, 2006, pp. 123 - 145.
  94. Wagh, R. S. (2013). Knowledge discovery from legal documents dataset using text mining techniques. International Journal of Computer Applications, 66(23).
  95. Wang, W. Y., Mayfield, E., Naidu, S., & Dittmar, J. (2012, July). Historical analysis of legal opinions with a sparse mixed-effects latent variable model. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 740-749).
  96. Westergaard, D., Stærfeldt, H. H., Tønsberg, C., Jensen, L. J., & Brunak, S. (2018). A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts. PLoS computational biology, 14(2), e1005962. https://doi.org/10.1371/journal.pcbi.1005962
  97. Whalen, R. (2016). Legal networks: The promises and challenges of legal network analysis. Michigan State Law Rev., 539–565.
  98. Yang, W., Jia, W., Zhou, X., & Luo, Y. (2019). Legal judgment prediction via multi-perspective bi-feedback network. https://doi.org/10.24963/ijcai.2019/567
  99. Ye, H., Jiang, X., Luo, Z., & Chao, W. (2018). Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions. https://doi.org/10.48550/arXiv.1802.08504
  100. Zabala, F. J., & Silveira, F. F. (2014). Jurimetria: estatística aplicada ao direito. Revista Direito e Liberdade, Natal, 16(1), 87-103.

Downloads

Download data is not yet available.