Online Essay Submissions

We appreciate your interest in submitting to Stanford Law Review Online. Unfortunately, the online submissions portal is currently closed and will reopen on Monday, January 9, 2023. We encourage you to submit at that time. Thank you!

Stanford Law Review Online submissions should be original pieces of timely scholarship on newsworthy topics and accessible to a wide audience. Submissions should be no longer than 3,000 words, inclusive of footnotes.  All submissions longer than 4,000 words will generally be automatically rejected, and the expectation is that essays longer than 3,000 words will be shortened during the editing process. Please submit an editable word processing document (.doc, .docx, .rtf, .otf, etc.) and NOT a PDF document.

All citations should be in Bluebook format. Lighter footnoting is encouraged; an essay of 3,000 words typically has in the ballpark of 30 footnotes. Please note that footnotes are required for (1) any material pulled directly from a source, including language or an idea from a source (2) any original materials (e.g., cases, statutes, newspaper stories, etc.) (3) any obscure materials that would be difficult for a reader to find. An example of the appropriate level of footnoting can be found here.

As our review process is blind, please remove your name and all identifying information from your submission. You may include your name on your cover letter/CV and in the form below. Note that our general ethics policy also applies to Stanford Law Review Online submissions.

Questions should be addressed to Samuel Wallace-Perdomo, Online Editor-in-Chief for Volume 75, at online [at] stanfordlawreview [dot] org.

Blinding Submissions

All identifying information, including the author’s name and any acknowledgements, must be removed prior to submission. Please consult the following links for information on how to remove identifying information, including metadata, from Microsoft Word documents: Word 2007Word 2010Word 2013, and Word 2016.

Note: Do not use this form to submit High Risk Data.