Guthrie's Guide to Better Legal Writing, 2nd ed
Bibliographic record
Abstract
The second edition of Guthrie’s Guide to Better Legal Writing is Neil Guthrie’s revised anthology of email queries and blog posts. The scope of the book is in its title: it offers practical tips and advice to legal writers. Guthrie’s definition of “legal writing” addresses written communication between lawyers, law students, and the layperson, although legal drafting is addressed intermittently. The book is not intended to be a comprehensive review of grammar and punctuation. Instead, it has an approximate agenda that is enhanced by the author’s personal narrative.\nThe author follows their own advice as outlined in the suggestions for choosing a writing topic (p. 2): \nThey write about something they practice and know. Guthrie has taught legal research and writing at the Faculty of Law, University of Toronto; has helped develop the legal research and writing curriculum for the Law Practice Program at Ryerson University; and is director of professional development, research, and knowledge management at Aird & Berlis LLP. \nThey recycle old work. This is the second edition of Guthrie’s Guide to Better Legal Writing. \nThey ensure pieces can be published in more than one place, with minor adjustments. Both editions of the book expand on a collection of emails that evolved into a continuing blog series on slaw.ca. \nThey get their writing in front of the right audience. According to WorldCat, the second edition is already available in most academic law libraries across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.082 | 0.053 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".