[{"description":"CALL member Erica Friesen, Research and Instruction Librarian & Online Learning Specialist at the Lederman Law Library at Queen's University in Kingston, Ontario recently wrote an article on \" Thoughts on the New Lexis+ Brief Analysis Tool: For Law Students and Novice Researchers \". It originally appeared on Slaw.ca on September 28, 2022. It is republished here with permission of the author. New academic year; new legal research tools. Something new always comes out right as another cohort of students is gearing up to begin their law degree. And, as with many new product launches these days, “artificial intelligence” is often a prominently displayed term with accompanying materials. As legal publishers continue to launch AI-driven research tools in Canada, what do students and other novice researchers need to know to be prepared for their first forays into legal research? Lexis recently launched the latest version of their legal research platform, Lexis+ Canada, for Canadian law schools. It features significant updates that employ artificial intelligence, including a “Brief Analysis” tool in line with similar products offered by Westlaw Edge (on the US platform) and vLex (already available in Canada). According to Lexis+, this tool helps researchers “expedite completion of briefs and other legal documents.” Once your document is uploaded, it spits out a report including the following categories: 1) recommended cases, 2) cases and legislation cited in your document, 3) jurisdiction, and 4) extracted and recommended concepts. While I do not intend to comment on the utility of this tool for practitioners, I have no doubt that students beginning law school or working in summer or articling positions are bound to encounter this new feature sooner or later. When you do, you may have questions: should I be using this tool? How or how not? The landing page for this tool in Lexis+ contains no information on how this tool works and the Lexis+ Canada “Help” database so far contains minimal documentation. Confusion or uncertainty is understandable with such opacity. To gain some insight, let’s run an experiment: take two case comments written by different authors on the same Supreme Court case from 2004. [1] An older decision means that we should retrieve an abundance of recommendations from the intervening 18 years. Both comments include an overview of the area of law at the time of this decision as well as an analysis of how this particular decision would change the area of law. Once Lexis analyzed these documents, I exported the resulting report and compared the results for each comment. Here are the two documents, in case you’d like to run the experiment yourself: Comment A: Teresa Scassa, “Recalibrating Copyright Law?: A Comment on the Supreme Court of Canada’s Decision in CCH Canadian Limited et al. v. Law Society of Upper Canada ”, Case Comment, (2004) 3:2 CJLT 89. Comment B: WL Hayhurst, “The Canadian Supreme Court on Copyright: CCH Canadian Ltd. v. Law Society of Upper Canada ”, Case Comment, (2004-2005) 41 Can Bus LJ 134. Recommended Cases “Recommendations” is the section of the Brief Analysis report that lists additional case law for you to consider adding to your legal document. Brief Analysis recommended 58 unique cases in total between Comment A and Comment B. However, only 10 of those cases were the same between the two documents (after adjusting for “recommended” cases that were actually cited in the other document). See Figure 1 . Key takeaway: Legal research and writing is an art, not a science. AI-driven tools like these run on the material that you upload, so you can expect substantially different results depending on the quality of analysis and the decisions you made in producing the original document. This does not mean that there is no value in using the tool, but you cannot expect it to help you build something from nothing. Recommendations are based on the content in the document,","id":3241473,"source":"Canadian Association of Law Libraries / L'Association canadienne des bibliothèques de droit CALL/ACBD Blog","titre":"CALL Member Article: Thoughts on the New Lexis+ Brief Analysis Tool: For Law Students and Novice Researchers","url":"https://www.callacbd.ca/CALL-Blog/12957419","pubDate":"Mon, 26 Oct 2026 07:55:49 +0100"},{"source":"Blog d'Arthur Perret","id":3047509,"description":"

This paper reports on an experiment which connects document design with document theory. Document design is about what documents should do. We use the example of document graphs and of Cosma, a visualization program created during the HyperOtlet research programme, to show how theory can inform design decisions and help conceptualize results. This paper is currently under peer review.

Document design or information design?

Findeli (2015) defines design as diagnostic and prescriptive, an example of “projective” epistemology: designers view the world as a project to realize, rather than an object to know. This definition of design implies that document design is less about what documents are and more about what they should do. This aligns with the evolution of document theory from an ontic view of documents to a functional one, which was pioneered by the European documentation movement in the first half of the twentieth century and reintroduced as “neo-documentation” in the literature during the 1990s.

Currently in the literature, document design is mostly considered to be a part of information design.1 The purpose of information design is to create effective communications (Renkema 2006 ; see also Pettersson 2002). Document design is usually taught within communications programs ; in the US, in either English or fine arts departments (Schriver 1997, p. 92‑93) ; in France, in Information and Communication Science departments.

Research on information design has very few connections with documentation. It has been criticized for its lack of theory: Carliner (2006, p. 10–14) warned about the field’s tendency to generalize findings despite shaky conceptual foundations and a limited understanding of topics such as the World Wide Web.2 We believe document theory can help address some of these challenges and bring perspective to information design.

Zacklad (2019) gives us an example of this. He draws from document theory to define information design as the interplay between textualization (creating content information), documentarization (shaping the medium) and auctorialization (creating author identities). Zacklad puts documentarization at the heart of information design (see fig. 1). Documentarization can be internal (the organization of fragments into documents) or external (the relational organization of documents, e.g. classification); it can involve different roles or positions (author, editor, printer/programmer, distributor, reader); it implies significant differences between designing the systems that involve documents (“system-oriented design”) and designing the documents themselves (“author design”).