{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:10:20Z","timestamp":1764850220038,"version":"3.46.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686387","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>To aid litigants\u2019 appeal strategies, this study introduces Disputability (\u03b4), a novel metric quantifying a case\u2019s contentiousness by the total number of judicial instances it undergoes. Using a dataset of 52,993 Taiwanese tax judgments, we test the hypothesis that similar cases share similar disputability levels. We compare Judgment-Level and Sentence-Level prediction models, finding that a Judgment-Level approach with multilingual-e5-base embeddings provides the strongest and most practical baseline. While the Sentence-Level model suffered from significant label noise, a high-precision kNN voting strategy proved particularly effective for identifying high-risk, disputable cases. Our work establishes an adaptable and extensible baseline for quantifying case disputability, offering a practical tool for computational law and legal analytics.<\/jats:p>","DOI":"10.3233\/faia251602","type":"book-chapter","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:05:19Z","timestamp":1764849919000},"source":"Crossref","is-referenced-by-count":0,"title":["Learning from the Judicial Journey: A Predictive Model for Case Disputability"],"prefix":"10.3233","author":[{"given":"Ho-Chien","family":"Huang","sequence":"first","affiliation":[{"name":"Data Science Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan"}]},{"given":"Chao-Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Chengchi University, Taipei, Taiwan"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251602","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:05:19Z","timestamp":1764849919000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"ISBN":["9781643686387"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251602","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]}}}