How Effective Is the Judiciary? Evidence on Correlation Between Cases’ Characteristics and Probability of Appeal

Authors

DOI:

https://doi.org/10.62355/ejels.24862

Keywords:

effectiveness, judiciary, probability of appeal, topic model

Abstract

This research proposes a way to assess judicial effectiveness, proxied by the probability of appeal of a decision. Focusing on the example of regional courts in Poland, it classifies cases based on their most accurate topic, creating a topic model on judgements. This classification is used to provide descriptive evidence on cases’ characteristics and their correlation with a higher or lower probability of appeal. The obtained results indicate that topic-based groups that are more heterogeneous in the legal departments of the associated cases are more likely to be appealed.

References

Abuzayed, A., & Al-Khalifa, H. (2021). BERT for Arabic topic modeling: An experimental study on BERTopic technique. Procedia computer science, 189, 191–194, DOI: 10.1016/j.procs.2021.05.096. DOI: https://doi.org/10.1016/j.procs.2021.05.096

Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American economic review, 91(5), 1369–1401, DOI: 10.1257/aer.91.5.1369. DOI: https://doi.org/10.1257/aer.91.5.1369

Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021). Transformer models for text-based emotion detection: a review of BERT-based approaches. Artificial Intelligence Review, 54, 5789–5829, DOI: 10.1007/s10462-021-09958-2. DOI: https://doi.org/10.1007/s10462-021-09958-2

Achenchabe, Y., & Akaaboune, M. (2021). Determinants of Judicial Efficiency in Morocco. Open Journal of Business and Management, 9(5), 2407–2424, DOI: 10.4236/ojbm.2021.95130. DOI: https://doi.org/10.4236/ojbm.2021.95130

Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of econometrics, 6(1), 21–37, DOI: 10.1016/0304-4076(77)90052-5. DOI: https://doi.org/10.1016/0304-4076(77)90052-5

Aletras, N., & Stevensson, M. (2013). Evaluating topic coherence using distributional semantics. In Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers, 13-22, URL: aclanthology.org/W13-0102.pdf.

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, DOI: 10.7717/peerj-cs.93. DOI: https://doi.org/10.7717/peerj-cs.93

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). Considerably improving clustering algorithms using UMAP dimensionality reduction technique: A comparative study. In International conference on image and signal processing, 317–325, DOI: 10.1007/978-3-030-51935-3_34. DOI: https://doi.org/10.1007/978-3-030-51935-3_34

Antonucci, L., Crocetta, C., & d’Ovidio, F. D. (2014). Evaluation of Italian judicial system. Procedia Economics and Finance, 17, 121-130, DOI: 10.1016/S2212-5671(14)00886-7. DOI: https://doi.org/10.1016/S2212-5671(14)00886-7

Ao, Z., Horváth, G., Sheng, C., Song, Y., & Sun, Y. (2023). Skill requirements in job advertisements: A comparison of skill-categorization methods based on wage regressions. Information Processing & Management, 60(2), 103185, DOI: 10.1016/j.ipm.2022.103185. DOI: https://doi.org/10.1016/j.ipm.2022.103185

Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A. (2016). A latent variable model approach to pmi-based word embeddings. Transactions of the Association for Computational Linguistics, 4, 385–399, DOI: 10.1162/tacl_a_00106. DOI: https://doi.org/10.1162/tacl_a_00106

Ash, E., Chen, D. L., & Galletta, S. (2022). Measuring Judicial Sentiment: Methods and Application to US Circuit Courts. Economica, 89(354), 362–376, DOI: 10.1111/ecca.12397. DOI: https://doi.org/10.1111/ecca.12397

Bahl, L. R., Jelinek, F., & Mercer, R. L. (1983). A maximum likelihood approach to continuous speech recognition. IEE transactions on pattern analysis and machine intelligence, 2, 179–190, DOI: 10.1109/TPAMI.1983.4767370. DOI: https://doi.org/10.1109/TPAMI.1983.4767370

Bai, H., Chen, Z., Lyu, M. R., King, I., & Xu, Z. (2018). Neural relational topic models for scientific article analysis. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 27–36, DOI: 10.1145/3269206.3271696. DOI: https://doi.org/10.1145/3269206.3271696

Balcerowicz, L. (2005). Post-communist transition: Some lessons. In IEA occasional paper, 127, URL: papers.ssrn.com/sol3/papers.cfm?abstract_id=676661. DOI: https://doi.org/10.2139/ssrn.676661

Banasik, P., Metelska-Szaniawska, K., Godlewska, M., & Morawska, S. (2021). Determinants of judges’ career choices and productivity: a Polish case study, European Journal of Law and Economics, 53, 81–107, DOI: 10.1007/s10657-021-09688-4. DOI: https://doi.org/10.1007/s10657-021-09688-4

Bełdowski, J., Dąbroś, Ł. & Wojciechowski, W. (2020). Judges and court performance: a case study of district commercial courts in Poland. European Journal of Law and Economics, 50, 171–201, DOI: 10.1007/s10657-020-09656-4. DOI: https://doi.org/10.1007/s10657-020-09656-4

Bhadury, A., Chen, J., Zhu, J., & Liu, S. (2016, April). Scaling up dynamic topic models. In Proceedings of the 25th International Conference on World Wide Web, 381–390, DOI: 10.1145/2872427.2883046. DOI: https://doi.org/10.1145/2872427.2883046

Bhat, M. R., Kundroo, M. A., Tarray, T. A., & Agarwal, B. (2020). Deep LDA: A new way to topic model. Journal of Information and Optimization Sciences, 41(3), 823–834, DOI: 10.1080/02522667.2019.1616911. DOI: https://doi.org/10.1080/02522667.2019.1616911

Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2020). Cross-lingual contextualized topic models with zero-shot learning. arXiv preprint, DOI: 10.48550/arXiv.2004.07737. DOI: https://doi.org/10.18653/v1/2021.eacl-main.143

Billiet, C. M., Blondiau, T., & Rousseau, S. (2014). Punishing environmental crimes: An empirical study from lower courts to the court of appeal. Regulation & Governance, 8(4), 472–496, DOI: 10.1111/rego.12044. DOI: https://doi.org/10.1111/rego.12044

Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of science. The annals of applied statistics, 1(1), 17–35, DOI: 10.1214/07-AOAS114. DOI: https://doi.org/10.1214/07-AOAS114

Blei, D. M., McAuliffe, J. D., Platt, J. C., Koller, D., Singer, Y., & Roweis, S. (2008). Supervised topic models advances. In Neural Information Processing Systems, 20, 121–128, URL: proceedings.neurips.cc/paper/2007/file/d56b9fc4b0f1be8871f5e1c40c0067e7-Paper.pdf.

Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of machine learning research, 3, 993–1022, URL: jmlr.org/papers/volume3/blei03a/blei03a.pdf?ref=http://githubhelp.com.

Boadway, R., & Bruce, N. (1984). A general proposition on the design of a neutral business tax. Journal of Public Economics, 24(2), 231–239, DOI: 10.1016/0047-2727(84)90026-4. DOI: https://doi.org/10.1016/0047-2727(84)90026-4

Boyd-Graber, J., & Blei, D. (2012). Multilingual topic models for unaligned text. arXiv preprint, DOI: 10.48550/arXiv.1205.2657.

Buocz, T. J. (2018). Artificial Intelligence in Court. Legitimacy Problems of AI Assistance in the Judiciary. Retskraft–Copenhagen Journal of Legal Studies, 2(1), 41–59, URL: static1.squarespace.com/static/59db92336f4ca35190c650a5/t/5ad9da5f70a6adf9d3ee842c/1524226655876/Artificial+Intelligence+in+Court.pdf.

Calvez, F., & Regis, N. (2007). Length of court proceedings in the member states of the Council of Europe based on the case law of the European Court of Human Rights. Council of Europe Publishing, 2nd edition, URL: marinacastellaneta.it/wp-content/uploads/2013/01/Rapport_2012_16_en.pdf.

Campello, R. J., Moulavi, D., & Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining, 160–172, DOI: 10.1007/978-3-642-37456-2_14. DOI: https://doi.org/10.1007/978-3-642-37456-2_14

Cao, Z., Li, S., Liu, Y., Li, W., & Ji, H. (2015). A novel neural topic model and its supervised extension. In Proceedings of the AAAI Conference on Artificial Intelligence, 29(1), 2210–2216, DOI: 10.1609/aaai.v29i1.9499. DOI: https://doi.org/10.1609/aaai.v29i1.9499

Carlson, K., Dadgostari, F., Livermore, M. A., & Rockmore, D. N. (2021). A multinetwork and machine learning examination of structure and content in the United States code. Frontiers in Physics, 8, 676, DOI: 10.3389/fphy.2020.625241. DOI: https://doi.org/10.3389/fphy.2020.625241

Carlson, K., Livermore, M. A., & Rockmore, D. N. (2020). The problem of data bias in the pool of published US appellate court opinions. Journal of Empirical Legal Studies, 17(2), 224–261, DOI: 10.1111/jels.12253. DOI: https://doi.org/10.1111/jels.12253

Carree, M., Günster, A., & Schinkel, M. P. (2010). European antitrust policy 1957-2004: an analysis of commission decisions. Review of Industrial Organization, 36(2), 97–131, DOI: 10.1007/s11151-010-9237-9. DOI: https://doi.org/10.1007/s11151-010-9237-9

Carter, D. J., Brown, J., & Rahmani, A. (2016). Reading the High Court at a distance: topic modelling the legal subject matter and judicial activity of the High Court of Australia, 1903-2015. The University of New South Wales Law Journal, 39(4), 1300–1354, URL: opus.lib.uts.edu.au/bitstream/10453/63528/1/394-2.pdf.

CEPEJ – European Commission for the Efficiency of Justice. (2018). European judicial systems: Efficiency and quality of justice. Strasbourg: Council of Europe, URL: rm.coe.int/rapport-avec-couv-18-09-2018-en/16808def9c.

Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J., & Blei, D. (2009). Reading tea leaves: How humans interpret topic models. Advances in neural information processing systems, 22, 1–9, URL: proceedings.neurips.cc/paper_files/paper/2009/file/f92586a25bb3145facd64ab20fd554ff-Paper.pdf.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444, DOI: 10.1016/0377-2217(78)90138-8. DOI: https://doi.org/10.1016/0377-2217(78)90138-8

Chien, J. T., & Lee, C. H. (2017). Deep unfolding for topic models. IEEE transactions on pattern analysis and machine intelligence, 40(2), 318–331, DOI: 10.1109/TPAMI.2017.2677439. DOI: https://doi.org/10.1109/TPAMI.2017.2677439

Coglianese, C., & Dor, L. M. B. (2020). AI in Adjudication and Administration. Brooklyn Law Review, 86, 791–838, URL: brooklynworks.brooklaw.edu/cgi/viewcontent.cgi?article=2272&context=blr.

Dadas, S., Perełkiewicz, M., & Poświata, R. (2020). Pre-training polish transformer-based language models at scale. In Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12–14, 2020, Proceedings, Part II 19 (pp. 301–314). Springer International Publishing, DOI: 10.1007/978-3-030-61534-5_27. DOI: https://doi.org/10.1007/978-3-030-61534-5_27

Dadgostari, F., Guim, M., Beling, P. A., Livermore, M. A., & Rockmore, D. N. (2021). Modeling law search as prediction. Artificial Intelligence and Law, 29, 3–34, DOI: 10.1007/s10506-020-09261-5. DOI: https://doi.org/10.1007/s10506-020-09261-5

Das, R., Zaheer, M., & Dyer, C. (2015). Gaussian LDA for topic models with word embeddings. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 795–804, DOI: 10.3115/v1/P15-1077. DOI: https://doi.org/10.3115/v1/P15-1077

De Groot, M., Aliannejadi, M., & Haas, M. R. (2022). Experiments on generalizability of BERTopic on multi-domain short text. arXiv preprint, DOI: 10.48550/arXiv.2212.08459.

Deeks, A. (2019). The judicial demand for explainable artificial intelligence. Columbia Law Review, 119(7), 1829–1850, URL: jstor.org/stable/26810851.

Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American society for information science, 41(6), 391–407, DOI: 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9. DOI: https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, DOI: 10.48550/arXiv.1810.04805.

Dieng, A. B., Ruiz, F. J., & Blei, D. M. (2020). Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics, 8, 439–453, DOI: 10.1162/tacl_a_00325. DOI: https://doi.org/10.1162/tacl_a_00325

Doan, T. N., & Hoang, T. A. (2021). Benchmarking neural topic models: An empirical study. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 4363–4368, URL: aclanthology.org/2021.findings-acl.382.pdf. DOI: https://doi.org/10.18653/v1/2021.findings-acl.382

Egger, R., & Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in sociology, 7, 886498, DOI: doi.org/10.3389/fsoc.2022.886498. DOI: https://doi.org/10.3389/fsoc.2022.886498

Eklund, J., Levratto, N., & Ramello, G. B. (2020). Entrepreneurship and failure: two sides of the same coin? Small Business Economics, 54, 373–382, DOI: 10.1007/s11187-018-0039-z. DOI: https://doi.org/10.1007/s11187-018-0039-z

ElBialy, N., & Garcia-Rubio, M. A. (2011). Assessing judicial efficiency of Egyptian first instance courts: A DEA analysis. MAGKS joint discussion paper series in economics, 19, 1–28, URL: econstor.eu/bitstream/10419/56541/1/657907855.pdf.

Espasa, M., & Esteller-More, A. (2015). Analyzing judicial courts’ performance: inefficiency vs. congestion. Revista de Economía Aplicada, 23(69), 61–82, URL: redalyc.org/pdf/969/96945385004.pdf

Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD Proceedings, Vol. 96, No. 34, 226–231, URL: cdn.aaai.org/KDD/1996/KDD96-037.pdf?source=post_page.

European Commission. (2021). The 2021 EU Justice Scoreboard. Brussels: European Commission, URL: commission.europa.eu/document/c6121790-3c0a-4b98-b49a-adc7cc9cd7c6_en?prefLang=pl.

Fusco, E., Laurenzi, M., & Maggi, B. (2021). Length of Trials in the Italian Judicial System: An Efficiency Analysis by Macro-Area. Justice System Journal, 42(1), 78–105, DOI: 10.1080/0098261X.2020.1852985. DOI: https://doi.org/10.1080/0098261X.2020.1852985

Garcia-Posada, M., & Mora-Sanguinetti, J. S. (2015). Does (average) size matter? Court enforcement, business demography and firm growth. Small Business Economics, 44(3), 639–669, DOI: 10.1007/s11187-014-9615-z. DOI: https://doi.org/10.1007/s11187-014-9615-z

Giacalone, M., Nissi, E., & Cusatelli, C. (2020). Dynamic efficiency evaluation of Italian judicial system using DEA based Malmquist productivity indexes. Socio-Economic Planning Sciences, 72, 100952, DOI: 10.1016/j.seps.2020.100952. DOI: https://doi.org/10.1016/j.seps.2020.100952

Glaeser, E. L., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2004). Do institutions cause growth?. Journal of economic Growth, 9, 271–303, DOI: 10.1023/B:JOEG.0000038933.16398.ed. DOI: https://doi.org/10.1023/B:JOEG.0000038933.16398.ed

Gozgor, G., Lau, C. K. M., Zeng, Y., & Lin, Z. (2019). The effectiveness of the legal system and inbound tourism. Annals of Tourism Research, 76, 24–35, DOI: 10.1016/j.annals.2019.03.003. DOI: https://doi.org/10.1016/j.annals.2019.03.003

Giacomelli, S., & Menon, C. (2013). Firm size and judicial efficiency: evidence from the neighbour’s court. Bank of Italy Temi di Discussione (Working Paper), 898, URL: citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e78423bdd50890504946809e125a68952ef9ff01.

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint, DOI: 10.48550/arXiv/2203.05794.

Hicks, J. R. (1939). The foundations of welfare economics. The economic journal, 49(196), 696–712, DOI: 10.2307/2225023. DOI: https://doi.org/10.2307/2225023

Hinton, G. E., & Roweis, S. (2002). Stochastic neighbor embedding. Advances in neural information processing systems, 15, 1–8, URL: proceedings.neurips.cc/paper_files/paper/2002/file/6150ccc6069bea6b5716254057a194ef-Paper.pdf.

Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 50–57, URL: dl.acm.org/doi/pdf/10.1145/312624.312649. DOI: https://doi.org/10.1145/312624.312649

Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6), 417, DOI: 10.1037/h0071325. DOI: https://doi.org/10.1037/h0071325

Hutama, L. B., & Suhartono, D. (2022). Indonesian Hoax News Classification with Multilingual Transformer Model and BERTopic. Informatica, 46(8), 81–90, DOI: 10.31449/inf.v46i8.4336. DOI: https://doi.org/10.31449/inf.v46i8.4336

Iqbal, M. I., Susanto, S., & Sutoro, M. (2019). Functionalization of E-Court System in Eradicating Judicial Corruption at The Level of Administrative Management. Jurnal Dinamika Hukum, 19(2), 370–388, DOI: 10.20884/1.jdh.2019.19.2.2510. DOI: https://doi.org/10.20884/1.jdh.2019.19.2.2510

Jansson, P., & Liu, S. (2017). Distributed representation, LDA topic modelling and deep learning for emerging named entity recognition from social media. In Proceedings of the 3rd Workshop on Noisy User-generated Text, 154–159, DOI: 10.18653/v1/W17-4420. DOI: https://doi.org/10.18653/v1/W17-4420

Jappelli, T., Pagano, M., & Bianco, M. (2005). Courts and banks: Effects of judicial enforcement on credit markets. Journal of Money, Credit and Banking, 37(2), 223–244, URL: jstor.org/stable/3838925. DOI: https://doi.org/10.1353/mcb.2005.0021

Jin, M., Luo, X., Zhu, H., & Zhuo, H. H. (2018). Combining deep learning and topic modeling for review understanding in context-aware recommendation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 1605–1614, DOI: 10.18653/v1/N18-1145. DOI: https://doi.org/10.18653/v1/N18-1145

Joachims, T. (1997). A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In ICML, 97, 143–151, URL: citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=c52eb66e23b201cb44f567cbb270feadca532c9a.

Kaldor, N. (1939). Welfare propositions of economics and interpersonal comparisons of utility. The economic journal, 49(195), 549–552, DOI: 10.2307/2224835. DOI: https://doi.org/10.2307/2224835

Kalyan, K. S., Rajasekharan, A., & Sangeetha, S. (2021). Ammus: A survey of transformer-based pretrained models in natural language processing. arXiv preprint, DOI: 10.48550/arXiv/2108.05542. DOI: https://doi.org/10.1016/j.jbi.2021.103982

Kittelsen, S. A., & Førsund, F. R. (1992). Efficiency analysis of Norwegian district courts. Journal of Productivity Analysis, 3(3), 277–306, https://doi.org/10.1007/BF00158357. DOI: https://doi.org/10.1007/BF00158357

Kłeczek, D. (2020). Polbert: Attacking polish nlp tasks with transformers. In Proceedings of the PolEval 2020 Workshop, pp. 79–88, URL: 2020.poleval.pl/files/poleval2020.pdf#page=79.

Kociołowicz-Wiśniewska, B., & Pilitowski B., & Burdziej, S. (2017). Ocena polskiego sądownictwa w świetle badań. Raport Fundacji Court Watch Polska, URL: monitorkonstytucyjny.eu/wp-content/uploads/2019/09/ocena_polskiego_sadownictwa_w_swietle_badan_vol_2.pdf.

Kornhauser, L. A. (1999). Appeal and supreme courts. In Encyclopedia of Law and Economics. Edward Elgar Publishing Limited, DOI: 10.4337/9781782540472.00007. DOI: https://doi.org/10.4337/9781782540472.00007

Kriz, V., & Hladka B. (2018). Czech legal text treebank 2.0. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation, 2387–2392, URL: aclanthology.org/L18-1713.pdf.

Kruczalak-Jankowska, J., Maśnicka, M., & Machnikowska, A. (2020). The relations between duration of insolvency proceedings and their efficiency (with a particular emphasis on Polish experiences). International Insolvency Review, 29(3), 379–392, DOI: 10.1002/iir.1392. DOI: https://doi.org/10.1002/iir.1392

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American statistical Association, 47(260), 583–621, URL: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=684ee7383ae4cc10a3b1d002f3cc97851521adc4. DOI: https://doi.org/10.1080/01621459.1952.10483441

Lacoste-Julien, S., Sha, F., & Jordan, M. (2008). DiscLDA: Discriminative learning for dimensionality reduction and classification. Advances in neural information processing systems, 21, 1-8, URL: proceedings.neurips.cc/paper_files/paper/2008/file/7b13b2203029ed80337f27127a9f1d28-Paper.pdf.

Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory and acquisition induction, and representation of knowledge. Psychological review, 104(2), 211, DOI: 10.1037/0033-295X.104.2.211. DOI: https://doi.org/10.1037//0033-295X.104.2.211

Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2–3), 259–284, DOI: 10.1080/01638539809545028. DOI: https://doi.org/10.1080/01638539809545028

Larochelle, H., & Lauly, S. (2012). A neural autoregressive topic model. Advances in Neural Information Processing Systems, 25, 1–9, URL: proceedings.neurips.cc/paper/2012/file/b495ce63ede0f4efc9eec62cb947c162-Paper.pdf.

Lauderdale, B. E., & Clark, T. S. (2014). Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science, 58(3), 754–771, DOI: 10.1111/ajps.12085. DOI: https://doi.org/10.1111/ajps.12085

Leibon, G., Livermore, M., Harder, R., Riddell, A., & Rockmore, D. (2018). Bending the law: geometric tools for quantifying influence in the multinetwork of legal opinions. Artificial Intelligence and Law, 26, 145–167, DOI: 10.1007/s10506-018-9224-2. DOI: https://doi.org/10.1007/s10506-018-9224-2

Lewin, A. Y., Morey, R. C. & Cook, T. J. (1982). Evaluating the administrative efficiency of courts. Omega, 10(4), 401–411, DOI: 10.1016/0305-0483(82)90019-6. DOI: https://doi.org/10.1016/0305-0483(82)90019-6

Li, W., & McCallum, A. (2006). Pachinko allocation: DAG-structured mixture models of topic correlations. In Proceedings of the 23rd international conference on Machine learning, 577–584, DOI: 10.1145/1143844.1143917. DOI: https://doi.org/10.1145/1143844.1143917

Li, C., Wang, H., Zhang, Z., Sun, A., & Ma, Z. (2016). Topic modeling for short texts with auxiliary word embeddings. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 165–174, DOI: 10.1145/2911451.2911499. DOI: https://doi.org/10.1145/2911451.2911499

Liu, Y., Liu, Z., Chua, T. S., & Sun, M. (2015). Topical word embeddings. In Proceedings of the AAAI Conference on Artificial Intelligence, 29(1), 2418–2424, DOI: 10.1609/aaai.v29i1.9522. DOI: https://doi.org/10.1609/aaai.v29i1.9522

Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). A survey of DEA applications. Omega, 41(5), 893–902, DOI: 10.1016/j.omega.2012.11.004. DOI: https://doi.org/10.1016/j.omega.2012.11.004

Livermore, M. A., Beling, P., Carlson, K., Dadgostari, F., Guim, M., & Rockmore, D. N. (2020). Law Search in the Age of the Algorithm. Michigan State Law Review, 5, 1183–1239, URL: uvalaw-scholarship.s3.amazonaws.com/2020.5_Livermore_FINAL.pdf.

Livermore, M. A., Riddell, A. B., & Rockmore, D. N. (2017). The Supreme Court and the judicial genre. Arizona Law Review, 59, 837–901, URL: ariddell.org/papers/livermore-rockmore-riddell-judical-genre-59arizlrev837.pdf. DOI: https://doi.org/10.2139/ssrn.2740126

Loza Mencia, E., & Furnkrantz, J. (2010). Efficient multilabel classification algorithms for large-scale problems in the legal domain. In Semantic Processing of Legal Texts, 23–32, DOI: 10.1007/978-3-642-12837-0_11. DOI: https://doi.org/10.1007/978-3-642-12837-0_11

Luz De Araujo, P. H., & De Campos, T. (2020). Topic modelling brazilian supreme court lawsuits. In Legal Knowledge and Information Systems, 113–122, DOI: 10.3233/FAIA200855. DOI: https://doi.org/10.3233/FAIA200855

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1(14), 281–297, URL: cs.cmu.edu/~bhiksha/courses/mlsp.fall2010/class14/macqueen.pdf.

Magalhães, P. C., & Garoupa, N. (2020). Judicial performance and trust in legal systems: Findings from a decade of surveys in over 20 European Countries. Social Science Quarterly, 101(5), 1743–1760, DOI: 10.1111/ssqu.12846. DOI: https://doi.org/10.1111/ssqu.12846

Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, 18(1), 50–60, URL: jstor.org/stable/2236101. DOI: https://doi.org/10.1214/aoms/1177730491

Marciano, A., Melcarne, A., & Ramello, G. B. (2019). The economic importance of judicial institutions, their performance and the proper way to measure them. Journal of Institutional Economics, 15(1), 81–98, DOI: 10.1017/S1744137418000292. DOI: https://doi.org/10.1017/S1744137418000292

Maxwell, T., & Schafer, B. (2010). Natural language processing and query expansion in legal information retrieval: challenges and a response. International Review of Law, Computers & Technology, 24(1), 63–72, DOI: 10.1080/13600860903570194. DOI: https://doi.org/10.1080/13600860903570194

Mazzei, D., & Ramjattan, R. (2022). Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling. Sensors, 22(22), 8641, DOI: 10.3390/s22228641. DOI: https://doi.org/10.3390/s22228641

McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint, DOI: 10.48550/arXiv.1802.03426. DOI: https://doi.org/10.21105/joss.00861

McKay, C. (2020). Predicting risk in criminal procedure: actuarial tools, algorithms, AI and judicial decision-making. Current Issues in Criminal Justice, 32(1), 22–39, DOI: 10.1080/10345329.2019.1658694. DOI: https://doi.org/10.1080/10345329.2019.1658694

Meeusen, W., & van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International economic review, 435–444, DOI: 10.2307/2525757. DOI: https://doi.org/10.2307/2525757

Mimno, D., Wallach, H., Naradowsky, J., Smith, D. A., & McCallum, A. (2009). Polylingual topic models. In Proceedings of the 2009 conference on empirical methods in natural language processing, 880–889, URL: aclanthology.org/D09-1092.pdf. DOI: https://doi.org/10.3115/1699571.1699627

Mimno, D., Wallach, H., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the 2011 conference on empirical methods in natural language processing, 262–272, URL: aclanthology.org/D11-1024.Pdf.

Moody, C. E. (2016). Mixing dirichlet topic models and word embeddings to make lda2vec. arXiv preprint, DOI: 10.48550/arXiv.1605.02019.

Mimno, D., & McCallum, A. (2012). Topic models conditioned on arbitrary features with dirichlet-multinomial regression. arXiv preprint, https://doi.org/10.48550/arXiv.1206.3278.

Mišćenić, E. (2019). The Effectiveness of Judicial Enforcement of the EU Consumer Protection Law. In Balkan Yearbook of European and International Law 2019, 129–153, DOI: 10.1007/16247_2019_8. DOI: https://doi.org/10.1007/16247_2019_8

Morison, J., & Harkens, A. (2019). Re-engineering justice? Robot judges, computerised courts and (semi) automated legal decision-making. Legal Studies, 39(4), 618–635, DOI: 10.1017/lst.2019.5. DOI: https://doi.org/10.1017/lst.2019.5

Mroczkowski, R., Rybak, P., Wróblewska, A., & Gawlik, I. (2021). HerBERT: Efficiently pretrained transformer-based language model for Polish. arXiv preprint, DOI: 10.48550/arXiv.2105.01735.

Newman, D., Lau, J. H., Grieser, K., & Baldwin, T. (2010). Automatic evaluation of topic coherence. In Human language technologies: The 2010 annual conference of the North American chapter of the association for computational linguistics, 100–108, URL: aclanthology.org/N10-1012.pdf.

Nguyen, D. Q., Sirts, K., & Johnson, M. (2015). Improving topic coherence with latent feature word representations in map estimation for topic modeling. In Proceedings of the Australasian Language Technology Association Workshop 2015, 116–121, URL: aclanthology.org/U15-1014.pdf.

OECD (2013). Judicial performance and its determinants: a cross-country perspective, OECD Economic Policy Paper, 5, DOI: 10.1787/5k44x00md5g8-en. DOI: https://doi.org/10.1787/5k44x00md5g8-en

Paatero, P., & Tapper, U. (1994). Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2), 111–126, DOI: 10.1002/env.3170050203. DOI: https://doi.org/10.1002/env.3170050203

Pareto, V. (1896). Cours d'économie politique: professé à l'Universi̧té de Lausanne (Vol. 1). F. Rouge.

Pearson, K. (1895). VII. Note on regression and inheritance in the case of two parents. Proceedings of the royal society of London, 58(347–352), 240–242, DOI: 10.1098/rspl.1895.0041. DOI: https://doi.org/10.1098/rspl.1895.0041

Pearson, K. (1900). X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157–175, DOI: 10.1080/14786440009463897. DOI: https://doi.org/10.1080/14786440009463897

Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2(11), 559–572, DOI: 10.1080/14786440109462720. DOI: https://doi.org/10.1080/14786440109462720

Qiang, J., Chen, P., Wang, T., & Wu, X. (2017). Topic modeling over short texts by incorporating word embeddings. In Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23–26, 2017, Proceedings, Part II 21, 363–374, DOI: 10.1007/978-3-319-57529-2_29. DOI: https://doi.org/10.1007/978-3-319-57529-2_29

Rabinovich, M., & Blei, D. (2014). The inverse regression topic model. In International Conference on Machine Learning, 199–207, URL: proceedings.mlr.press/v32/rabinovich14.pdf.

Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. Preprint 1–12, URL: mikecaptain.com/resources/pdf/GPT-1.pdf.

Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009, August). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 conference on empirical methods in natural language processing, pp. 248–256, URL: aclanthology.org/D09-1026.pdf. DOI: https://doi.org/10.3115/1699510.1699543

Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning, 242(1), 29–48, URL: citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b3bf6373ff41a115197cb5b30e57830c16130c2c.

Re, R. M., & Solow-Niederman, A. (2019). Developing artificially intelligent justice. Stanford Technology Law Review, 22, 242–289, URL: law.stanford.edu/wp-content/uploads/2019/08/Re-Solow-Niederman_20190808.pdf.

Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084, DOI: 10.48550/arXiv.1908.10084. DOI: https://doi.org/10.18653/v1/D19-1410

Roder, M., Both, A., & Hinnenburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the eight ACM international conference on Web search and data mining, 399–408, DOI: 10.1145/2684822.2685324. DOI: https://doi.org/10.1145/2684822.2685324

Rodrik, D., Subramanian, A., & Trebbi, F. (2004). Institutions rule: the primacy of institutions over geography and integration in economic development. Journal of economic growth, 9, 131–165, DOI: 10.1023/B:JOEG.0000031425.72248.85. DOI: https://doi.org/10.1023/B:JOEG.0000031425.72248.85

Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2012). The author-topic model for authors and documents. arXiv preprint, DOI: 10.48550/arXiv.1207.4169.

Rybak, P., Mroczkowski, R., Tracz, J., & Gawlik, I. (2020). KLEJ: Comprehensive benchmark for Polish language understanding. arXiv preprint, DOI: 10.48550/arXiv.2005.00630. DOI: https://doi.org/10.18653/v1/2020.acl-main.111

Salihu, H. A., & Gholami, H. (2018). Mob justice, corrupt and unproductive justice system in Nigeria: An empirical analysis. International Journal of Law, Crime and Justice, 55, 40–51, DOI: 10.1016/j.ijlcj.2018.09.003. DOI: https://doi.org/10.1016/j.ijlcj.2018.09.003

Samaha, A. M., Heise, M., & Sisk, G. C. (2020). Inputs and Outputs on Appeal: An Empirical Study of Briefs, Big Law, and Case Complexity. Journal of Empirical Legal Studies, 17(3), 519–555, DOI: 10.1111/jels.12263. DOI: https://doi.org/10.1111/jels.12263

Sangaraju, V. R., Bolla, B. K., Nayak, D. K., & Kh, J. (2022). Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings. arXiv preprint, DOI: 10.48550/arXiv.2205.07259.

Santolino, M. (2010). Determinants of the decision to appeal against motor bodily injury judgements made by Spanish trial courts. International Review of Law and Economics, 30(1), 37–45, DOI: 10.1016/j.irle.2009.09.002. DOI: https://doi.org/10.1016/j.irle.2009.09.002

Santos, S. P., & Amado, C. A. (2014). On the need for reform of the Portuguese judicial system – Does Data Envelopment Analysis assessment support it? Omega, 47, 1–16, DOI: 10.1016/j.omega.2014.02.007. DOI: https://doi.org/10.1016/j.omega.2014.02.007

Scarpino, I., Zucco, C., Vallelunga, R., Luzza, F., & Cannataro, M. (2022). Investigating topic modeling techniques to extract meaningful insights in Italian long COVID narration. BioTech, 11(3), 41–55, DOI: 10.3390/biotech11030041. DOI: https://doi.org/10.3390/biotech11030041

Schmitz, A. J. (2019). Expanding access to remedies through E-court initiatives. Buffalo Law Review, 67, 89–163, URL: digitalcommons.law.buffalo.edu/cgi/viewcontent.cgi?article=4724&context=buffalolawreview.

Sharma, D., Kumar, B., & Chand, S. (2017). A survey on journey of topic modeling techniques from SVD to deep learning. International Journal of Modern Education and Computer Science, 9(7), 50, DOI: 10.5815/ijmecs.2017.07.06. DOI: https://doi.org/10.5815/ijmecs.2017.07.06

Shi, M., Liu, J., Zhou, D., Tang, M., & Cao, B. (2017). WE-LDA: a word embeddings augmented LDA model for web services clustering. In 2017 ieee international conference on web services (icws), 9–16, DOI: 10.1109/ICWS.2017.9. DOI: https://doi.org/10.1109/ICWS.2017.9

Siemaszko, A., Ostaszewski, P., Klimczak, J., & Włodarczyk-Madejska, J. (2019). Sense of security among residents of Warsaw. Survey results. Law in action, 38, 140–158, DOI: 10.32041/pwd.3809. DOI: https://doi.org/10.32041/PWD.3809

Smuda, F., Bougette, P., & Hüschelrath, K. (2015). Determinants of the duration of European appellate court proceedings in cartel cases. Journal of Common Market Studies, 53(6), 1352–1369, DOI: 10.1111/jcms.12259. DOI: https://doi.org/10.1111/jcms.12259

Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72–101, URL: digamoo.free.fr/spearman1904a.pdf. DOI: https://doi.org/10.2307/1412159

Stevens, K., Kegelmeyer, P., Andrzejewski, D., & Buttler, D. (2012). Exploring topic coherence over many models and many topics. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, 952–961, URL: aclanthology.org/D12-1087.pdf.

Terragni, S., Fersini, E., Galuzzi, B. G., Tropeano, P., & Candelieri, A. (2021). OCTIS: Comparing and optimizing topic models is simple!. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 263–270, DOI: 10.18653/v1/2021.eacl-demos.31. DOI: https://doi.org/10.18653/v1/2021.eacl-demos.31

Thompson, L., & Mimno, D. (2020). Topic modeling with contextualized word representation clusters. arXiv preprint, DOI: 10.48550/arXiv.2010.12626.

Ulenaers, J. (2020). The Impact of Artificial Intelligence on the Right to a Fair Trial: Towards a Robot Judge?. Asian Journal of Law and Economics, 11(2), DOI: 10.1515/ajle-2020-0008. DOI: https://doi.org/10.1515/ajle-2020-0008

Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11), 2579–2605, URL: jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf.

Virtucio, M. B. L., Aborot, J. A., Abonita, J. K. C., Avinante, R. S., Copino, R. J. B., Neverida, M. P., Osiana, V. O. (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 (COMPSAC), 2, 130–135, DOI: 10.1109/COMPSAC.2018.10348. DOI: https://doi.org/10.1109/COMPSAC.2018.10348

Voigt, S. (2016). Determinants of judiciary efficiency: A survey. European Journal of Law and Economics, 42(2), 183–208, DOI: 10.1007/s10657-016-9531-6. DOI: https://doi.org/10.1007/s10657-016-9531-6

Wachter, S., Mittelstadt, B., & Russell, C. (2021). Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. Computer Law & Security Review, 41, 105567, DOI: 10.1016/j.clsr.2021.105567. DOI: https://doi.org/10.1016/j.clsr.2021.105567

Wang, X., & Yang, Y. (2020). Neural topic model with attention for supervised learning. In International Conference on Artificial Intelligence and Statistics, 1147–1156, URL: proceedings.mlr.press/v108/wang20c/wang20c.pdf.

Woliński, M. (2014). Morfeusz reloaded. In Proceedings of the ninth international conference on language resources and evaluation, LREC, 1106–1111, URL: citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=a6115f4593e7da10d5d990c42ee5c242a7c56058.

Xun, G., Gopalakrishnan, V., Ma, F., Li, Y., Gao, J., & Zhang, A. (2016). Topic discovery for short texts using word embeddings. In 2016 IEEE 16th international conference on data mining (ICDM), 1299–1304, DOI: 10.1109/ICDM.2016.0176. DOI: https://doi.org/10.1109/ICDM.2016.0176

Yeung, L. L., & Azevedo, P. F. (2011). Measuring efficiency of Brazilian courts with data envelopments analysis (DEA). IMA Journal of Management Mathematics, 22(4), 343–356, DOI: 10.1093/imaman/dpr002. DOI: https://doi.org/10.1093/imaman/dpr002

Zankadi, H., Idrissi, A., Daoudi, N., & Hilal, I. (2022). Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques. Education and Information Technologies, 1–18, DOI: 10.1007/s10639-022-11373-1. DOI: https://doi.org/10.1007/s10639-022-11373-1

Zhao, H., Phung, D., Huynh, V., Jin, Y., Du, L., & Buntine, W. (2021). Topic modelling meets deep neural networks: A survey. arXiv preprint, DOI: 10.48550/arXiv.2103.00498. DOI: https://doi.org/10.24963/ijcai.2021/638

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2024-11-17

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Świtała, M. (2024). How Effective Is the Judiciary? Evidence on Correlation Between Cases’ Characteristics and Probability of Appeal. European Journal of Empirical Legal Studies, 1(2), 179–206. https://doi.org/10.62355/ejels.24862

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