The Virginia Law Review invites eligible UVA law students who would like their work to be considered for publication in the Law Review to submit Notes three times per year: January, March, and October. Please check the Announcements page for precise dates and submission instructions. For further information and guidance, please refer to the Student Scholarship Manual here. For Notes Advising, please fill out this form.
Eligibility Requirements: Notes are student-written articles. The Virginia Law Review accepts Note submissions from current J.D. candidates at the University of Virginia and from recent graduates who have received a J.D. from the University of Virginia within the preceding twelve months.
Publication: There are three windows of time during which students may submit a Note for publication: January, March, and September. Approximately 8 Notes are selected for publication each year on the basis of two primary factors, novel legal analysis that contributes to the field of legal scholarship and quality of writing. Authors who are not members of the Law Review whose notes are accepted for publication prior to March 1 of their final year at the law school will be invited to join. First year law students whose Notes are accepted for publication will become members of the Law Review at the beginning of their second year.
Notes Advising: The Law Review offers Notes Advising to all students who are interested in submitting a Note for publication, whether or not those students are current members of the Law Review. Past experience suggests that students greatly benefit from Notes Advising, and Notes selected for publication have frequently gone through the advising process. Notes Advisors are available to answer questions about the submission process and meet with students at any stage of the writing process (selecting a topic, reviewing an existing draft, etc.). However, pursuant to the University of Virginia’s Honor Code, we are unable to give substantive advice on any paper that will be submitted for a grade until the student has turned it in to his or her professor. Students interested in Notes Advising may fill out this form and will be contacted promptly by a Notes Advisor. Please note that new requests for advising will not be entertained during open Notes Pools. This policy is intended to ensure that authors have sufficient time to implement suggested changes before submission deadlines.
Selecting a Note Topic: Notes may be on any topic of the student’s choosing, so long as it is related to the law and is not preempted by an already published piece (in the Virginia Law Review or elsewhere). Interested students should begin their research by conducting a preemption check, which seeks to answer two basic questions. First, has the topic already been covered by previous publications? In most cases, this question is answered by whether or not another published Article or Note has addressed the same issue. If another piece has been published on the topic, the topic is not preempted if the Note will present a sufficiently different viewpoint (including significant updates of legal thought or case law) so as to retain its usefulness. Second, is the topic too broad or too narrow? The topic should be framed so that it is broad enough to evoke interest, yet narrow enough to be manageable. When performing a preemption check, consult the following: major treatises; at least five years of the Index to Legal Periodicals and the loose-leaf Current Index to Legal Periodicals; major cases; topical reporters; and Journal Articles and Notes.
Length: Notes must be between 10,000–15,500 words inclusive of footnotes. The word limit does not include any information on the cover page, such as the table of contents or abstract. Notes longer than 15,500 words or less than 10,000 words will not be considered without prior approval of the Notes Development Editor.
Submission Details: Notes must be submitted via email to valrnotes@gmail.com. Notes will not be considered for publication unless the submissions follow specific guidelines. Review the submission instructions in the most recent Notes Pool posting on the Announcements page.
Graded Submissions: Per the University’s Academic Policies, no student may submit a Note or Comment to the Virginia Law Review during the same semester the written work is submitted for a grade without prior approval from the Assistant Dean for Academic Services and Notes Development Editor.
Artificial Intelligence and the Virginia Law Review’s Tradition of Excellence
The Virginia Law Review has a long tradition of promoting excellence in legal scholarship. Central to this tradition are the originality and human-driven nature of our scholarship. The advent of generative artificial intelligence (AI) has created challenges for the field of legal scholarship. While we recognize that AI is now part of the academic landscape, we are committed to maintaining the journal’s tradition of publishing rigorous, accurate, and original scholarship.
Artificial Intelligence Disclosure
The Virginia Law Review requires authors to disclose the use of AI upon submitting their article for review. We emphasize that the use of AI is not disqualifying. Disclosure of AI facilitates our ability to thoroughly evaluate a piece, and it promotes trust between the author and the journal.
Authors must complete a Google Form to disclose any substantive use of AI. By “substantive,” we mean the use of AI to support an author’s factual assertions, legal claims, or overall argument, such as generating relevant cases, writing or revising textual sentences, or drafting parentheticals. The use of grammar or spelling assistance services such as Grammarly or spell-checkers is not considered “substantive.”
Authors should err on the side of disclosure. The content selection committee prefers more detailed disclosures that clearly identify where and how authors used AI. The more detailed the disclosure, the more confidently we can extend a publication offer.
Authors must separate their disclosure into the following categories:
- Research:
- E.g., identifying relevant cases and quotations, summarizing relevant secondary sources, summarizing areas of law for background knowledge, generating arguments and counterarguments
- Writing:
- E.g., drafting or revising main body text, drafting footnote text, drafting explanatory parentheticals
- Other:
- E.g., conducting statistical analysis, generating tables/graphs, generating images
Within each category, authors should also indicate what AI tools they used and the extent to which they verified or built upon AI-generated material. Authors may find sample disclosures on the next page.
We reserve the right to rescind an offer for publication if, during the editing process, we discover undisclosed AI usage that compromises the integrity of the piece, including/especially hallucinated sources or quotations. If appropriate, we may also require the disclosure of AI usage in a star footnote.
Sample Disclosures
Research (e.g., identifying relevant cases and quotations, summarizing relevant secondary sources, summarizing areas of law for background knowledge, generating arguments and counterarguments)
- I used Westlaw Deep Research to identify state-level parallels to cases on whistleblower protections. My research assistants and I verified the holdings and quotations for these cases. This research informed the discussion in Section I.C around footnotes 120–25.
- I used ChatGPT to provide background context in admiralty law and to identify helpful historical treatises on the subject. I used this research to write the two paragraphs in Section I.A about 17th-/18th-century developments in commercial law.
Writing (e.g., drafting or revising main body text, drafting footnote text, drafting explanatory parentheticals)
- I used Claude to revise my writing in Part II. I instructed Claude to use a punchy style. I verified that Claude did not change the substantive claims of any AI-revised paragraph.
- I used ChatGPT to generate explanatory parentheticals for the sources in footnotes 200–12. I verified the accuracy of each explanatory parenthetical.
Other (e.g., conducting statistical analysis, generating tables/graphs, generating images)
- I used Claude to analyze my data set (at Appendix A) and test for statistical significance at the 0.05 level; I discuss this analysis in Section IV.A. I then consulted members of the University of Viginia Legal Data Lab to conduct the more complex analysis in Sections IV.B-C.
Please contact the Notes Development Editor, Lauren Palmiter (ggf8zw@virginia.edu), with any questions.