[{"pubDate":"Fri, 30 Oct 2026 13:56:53 +0100","titre":"The First-tier Tribunal for Scotland (Transfer of Functions and Members of the Police Appeals Tribunal) Amendment Regulations 2026","url":"http://www.legislation.gov.uk/id/ssi/2026/321http://www.legislation.gov.uk/ssi/2026/321/madehttp://www.legislation.gov.uk/ssi/2026/321/made/data.xmlhttp://www.legislation.gov.uk/ssi/2026/321/made/data.rdfhttp://www.legislation.gov.uk/ssi/2026/321/made/data.aknhttp://www.legislation.gov.uk/ssi/2026/321/made/data.xhthttp://www.legislation.gov.uk/ssi/2026/321/made/data.htmlhttp://www.legislation.gov.uk/ssi/2026/321/made/data.htmhttp://www.legislation.gov.uk/ssi/2026/321/made/data.csvhttp://www.legislation.gov.uk/ssi/2026/321/made/data.pdfhttp://www.legislation.gov.uk/ssi/2026/321/contents/made","id":3253185,"description":"These Regulations amend the First-tier Tribunal for Scotland (Transfer of Functions and Members of the Police Appeals Tribunal) Regulations 2025 (“the 2025","source":"UK Legilsation News"},{"titre":"LC v Secretary of State for Work and Pensions (anonymity order)","url":"https://www.judiciary.uk/judgments/lc-v-secretary-of-state-for-work-and-pensions-anonymity-order/","pubDate":"Thu, 29 Oct 2026 08:01:50 +0100","source":"Judgement Courts Tribunals England Wales","description":"UT ref: UA-2026-000083-PIP In the Upper Tribunal Administrative Appeals Chamber 28 October 2026 Before: Stewart Wright Judge of the Upper Tribunal Between: LC -v- Secretary of State for Work and Pensions Anonymity Order Appellant: LC Respondents: Secretary of State for Work and Pensions Tribunal: First tier Tribunal (Social Entitlement Chamber) Tribunal venue: Scarborough Tribunal Case Nos: SC231/24/00181 Tribunal decision date: 29 August 2025 NOTICE: Any breach of this order is liable to be treated as a contempt of court and may be punishable by imprisonment, fine or other sanctions under section 25 of the Tribunals, Courts and Enforcement Act 2007. The maximum punishment that may be imposed is a sentence of two years’ imprisonment or an unlimited fine. It is ordered , under rule 14(1)(b) of the Tribunal Procedure (Upper Tribunal Rules) 2008, that, without the permission of this Tribunal, no one shall disclose or publish the name, or any part of the address, of the appellant in these proceedings or publish or reveal any other information that would be likely to lead to the identification of the appellant. The appellant shall be known as LC. Anyone who objects to this order may apply to the Upper Tribunal for it to be varied or set aside. A copy of this Order shall be published on the website of the Judiciary of England and Wales. REASONS This anonymity order is made on the application of the appellant. It is sought because “the appeal involves a disabled claimant and details of their disability are to reasonably be inferred from a prospectively publicised decision”. I have by a separate decision allowed the appellant’s appeal to the Upper Tribunal. That decision will be published on public facing websites and, if no anonymity order is made, the appellant would be named in that decision. The principle of open justice is of fundamental importance to the justice system. An aspect of open justice is that parties’ names are made public. Derogation from this principle requires justification. Particular regard must be given to the importance of the right to freedom of expression, including the right to publish reports of cases. In this case, I am satisfied that it is necessary to derogate from the open justice principle to the limited extent of not naming the appellant. The appellant has ADHD and anxiety and depression and those features of his health and conditions need to be referred to in the decision on the appeal. In that context, I am satisfied that an anonymity order should be made because I accept a risk of harm could arise for this vulnerable appellant if he was to be identified in these proceedings, because those health details would then be in the public domain. The public interest in open justice and freedom of expression will still be served by this order and the final decision in this appeal being published online. The letters LC are not the appellant’s initials. The post LC v Secretary of State for Work and Pensions (anonymity order) appeared first on Courts and Tribunals Judiciary","id":3248599},{"id":3243717,"description":"These Regulations amend the First-tier Tribunal for Scotland Housing and Property Chamber (Procedure) Regulations 2017 (S.S.I. 2017/328) (“the principal Regulations”) to reflect the conferral of new functions on the First-tier Tribunal by sections 16 and 17 of the Housing (Scotland) Act 2025 (asp 13) (“the 2025 Act”). Section 14 of the 2025 Act gives local authorities and the Scottish Ministers powers to seek information from a landlord relating to the tenancy of a house included in the landlord’s entry in the landlord register. These Regulations make provision for an application to be made to the First-tier Tribunal by a local authority or the Scottish Ministers where an information holder fails to provide information without reasonable excuse, or provides information that the information holder must have known to be false in a material","source":"UK Legilsation News","pubDate":"Tue, 27 Oct 2026 07:56:00 +0100","titre":"The First-tier Tribunal for Scotland Housing and Property Chamber (Procedure) Amendment Regulations 2026","url":"http://www.legislation.gov.uk/id/ssi/2026/288http://www.legislation.gov.uk/ssi/2026/288/madehttp://www.legislation.gov.uk/ssi/2026/288/made/data.xmlhttp://www.legislation.gov.uk/ssi/2026/288/made/data.rdfhttp://www.legislation.gov.uk/ssi/2026/288/made/data.aknhttp://www.legislation.gov.uk/ssi/2026/288/made/data.xhthttp://www.legislation.gov.uk/ssi/2026/288/made/data.htmlhttp://www.legislation.gov.uk/ssi/2026/288/made/data.htmhttp://www.legislation.gov.uk/ssi/2026/288/made/data.csvhttp://www.legislation.gov.uk/ssi/2026/288/made/data.pdfhttp://www.legislation.gov.uk/ssi/2026/288/contents/made"},{"titre":"Small Language Models for Legislative Summarization: An Empirical Evaluation of Performance and Suitability","url":"https://ieeexplore.ieee.org/document/11460158","pubDate":"Wed, 22 Apr 2026 10:44:53 +0200","description":"Parliamentary institutions generate extensive, domain-specific legislative documents, including normative texts and parliamentary debate transcripts. These documents differ in content and linguistic complexity, making automatic summarization essential for producing coherent summaries aligned with institutional standards. While large language models (LLMs) achieve high summarization quality, their computational requirements limit deployment in parliamentary and public-sector environments. In contrast, small language models (SLMs) offer a more resource-efficient alternative, but their capabilities and performance relative to LLMs, extractive methods, and other SLMs remain underexplored. In this work, we present the first comprehensive evaluation of SLMs for legislative summarization, assessing their effectiveness across document types and languages. We use two complementary datasets: EUR-Lex-Sum, a multilingual corpus of normative texts covering six European languages, and ParcanDeb-Sum, a Spanish dataset of parliamentary debate records aligned with expert-written summaries. Summary quality is evaluated through a three-tier framework combining automatic metrics (ROUGE and BERTScore), LLM-based qualitative assessment, and expert-guided evaluation formalizing parliamentary debate summarization criteria. Our results show that: 1) instruction-tuned SLMs consistently outperform extractive baselines and, in several settings, rival LLMs with seven to eight billion parameters; 2) performance differs by document type, with fine-tuning being critical for debate transcripts, whereas instruction-tuning alone suffices for normative texts; and 3) for normative texts, SLMs establish a new benchmark for multilingual performance, while for parliamentary debates, fine-tuned SLMs achieve performance comparable to domain experts. These findings provide empirical evidence that high-quality legislative summarization can be achieved with SLMs, offering actionable guidance for selecting models that balance performance with computational constraints. Parliamentary institutions generate extensive, domain-specific legislative documents, including normative texts and parliamentary debate transcripts. These documents differ in content and linguistic complexity, making automatic summarization essential for producing coherent summaries aligned with institutional standards. While large language models (LLMs) achieve high summarization quality, their computational requirements limit deployment in parliamentary and public-sector environments. In contrast, small language models (SLMs) offer a more resource-efficient alternative, but their capabilities and performance relative to LLMs, extractive methods, and other SLMs remain underexplored. In this work, we present the first comprehensive evaluation of SLMs for legislative summarization, assessing their effectiveness across document types and languages. We use two complementary datasets: EUR-Lex-Sum, a multilingual corpus of normative texts covering six European languages, and ParcanDeb-Sum, a Spanish dataset of parliamentary debate records aligned with expert-written summaries. Summary quality is evaluated through a three-tier framework combining automatic metrics (ROUGE and BERTScore), LLM-based qualitative assessment, and expert-guided evaluation formalizing parliamentary debate summarization criteria. Our results show that: 1) instruction-tuned SLMs consistently outperform extractive baselines and, in several settings, rival LLMs with seven to eight billion parameters; 2) performance differs by document type, with fine-tuning being critical for debate transcripts, whereas instruction-tuning alone suffices for normative texts; and 3) for normative texts, SLMs establish a new benchmark for multilingual performance, while for parliamentary debates, fine-tuned SLMs achieve performance comparable to domain experts. These findings provide empirical evidence that high-quality legislative summarization can be achieved with SLMs, offering actionable guidance for selecting models that balance performance with","id":2841540,"source":"Scoop.it - Documentation juridique (Stéphan Cottin)"}]