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Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005

Safia Kanwal, Livio Robaldo Orcid Logo, Hafsa Dar, Davide Liga, Joseph Anim, kuuku Anim

Frontiers in Artificial Intelligence and Applications, Volume: 416, Pages: 412 - 414

Swansea University Authors: Safia Kanwal, Livio Robaldo Orcid Logo, kuuku Anim

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DOI (Published version): 10.3233/faia251622

Abstract

In the devolved legal system of the United Kingdom (UK), legislative provisions may apply differently across regions such as England, Wales, Scotland, and Northern Ireland. Accurately determining this territorial scope is essential for legal interpretation and AI-assisted legal tools. However, metad...

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Published in: Frontiers in Artificial Intelligence and Applications
ISBN: 9781643686387
ISSN: 0922-6389 1879-8314
Published: Amsterdam IOS Press 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70879
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spelling 2026-01-15T11:01:50.0479417 v2 70879 2025-11-12 Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005 c6c825948e8c21cf07dbff0709ab2ec6 Safia Kanwal Safia Kanwal true false b711cf9f3a7821ec52bd1e53b4f6cf9e 0000-0003-4713-8990 Livio Robaldo Livio Robaldo true false bf3b60b3f57a5da2add996068307355e kuuku Anim kuuku Anim true false 2025-11-12 HRCL In the devolved legal system of the United Kingdom (UK), legislative provisions may apply differently across regions such as England, Wales, Scotland, and Northern Ireland. Accurately determining this territorial scope is essential for legal interpretation and AI-assisted legal tools. However, metadata capturing jurisdictional applicability is inconsistently format, as only a few Acts include Territorial Application Annexes. This study presents a case study using the Education Act 2005 to evaluate the accuracy of automated methods for identifying territorial scope. We found that only 46.9% of sections matched in jurisdictional coverage. The best-performing approach achieved nearly 80% accuracy, showing that LLMs can effectively support scalable and explainable territorial disambiguation. Conference Paper/Proceeding/Abstract Frontiers in Artificial Intelligence and Applications 416 412 414 IOS Press Amsterdam 9781643686387 0922-6389 1879-8314 Territorial Disambiguation, Legal—DocML, Large Language Models 2 12 2025 2025-12-02 10.3233/faia251622 COLLEGE NANME Hillary Rodham Clinton Law School COLLEGE CODE HRCL Swansea University Not Required 2026-01-15T11:01:50.0479417 2025-11-12T08:15:31.3899983 Faculty of Humanities and Social Sciences Hilary Rodham Clinton School of Law Safia Kanwal 1 Livio Robaldo 0000-0003-4713-8990 2 Hafsa Dar 3 Davide Liga 4 Joseph Anim 5 kuuku Anim 6 70879__35999__86b0b912335f4b4fa56ffe0893975e89.pdf 70879.VoR.pdf 2026-01-15T10:55:54.5872330 Output 162237 application/pdf Version of Record true © 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). true eng https://creativecommons.org/licenses/by-nc/4.0/deed.en_US
title Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005
spellingShingle Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005
Safia Kanwal
Livio Robaldo
kuuku Anim
title_short Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005
title_full Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005
title_fullStr Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005
title_full_unstemmed Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005
title_sort Foundations for Territorial Disambiguation in Law: A Preliminary Study Using the Education Act 2005
author_id_str_mv c6c825948e8c21cf07dbff0709ab2ec6
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author_id_fullname_str_mv c6c825948e8c21cf07dbff0709ab2ec6_***_Safia Kanwal
b711cf9f3a7821ec52bd1e53b4f6cf9e_***_Livio Robaldo
bf3b60b3f57a5da2add996068307355e_***_kuuku Anim
author Safia Kanwal
Livio Robaldo
kuuku Anim
author2 Safia Kanwal
Livio Robaldo
Hafsa Dar
Davide Liga
Joseph Anim
kuuku Anim
format Conference Paper/Proceeding/Abstract
container_title Frontiers in Artificial Intelligence and Applications
container_volume 416
container_start_page 412
publishDate 2025
institution Swansea University
isbn 9781643686387
issn 0922-6389
1879-8314
doi_str_mv 10.3233/faia251622
publisher IOS Press
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str Hilary Rodham Clinton School of Law{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}Hilary Rodham Clinton School of Law
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description In the devolved legal system of the United Kingdom (UK), legislative provisions may apply differently across regions such as England, Wales, Scotland, and Northern Ireland. Accurately determining this territorial scope is essential for legal interpretation and AI-assisted legal tools. However, metadata capturing jurisdictional applicability is inconsistently format, as only a few Acts include Territorial Application Annexes. This study presents a case study using the Education Act 2005 to evaluate the accuracy of automated methods for identifying territorial scope. We found that only 46.9% of sections matched in jurisdictional coverage. The best-performing approach achieved nearly 80% accuracy, showing that LLMs can effectively support scalable and explainable territorial disambiguation.
published_date 2025-12-02T05:24:30Z
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