Book Review: Lawyers Gone Bad: Money, Sex and Madness in Canada's Legal Profession, by Philip Stayton
Bibliographic record
Abstract
DID PHILIP SLAYTON intend the brouhaha that accompanied the release of his book in the summer of 2007?Controversy does sell books, after all.In the world of fiction, Dan Brown's controversial book, The Da Vinci Code, 3 created a commotion and became a phenomenal bestseller.Of course, there are more differences than similarities between The Da Vinci Code and Lawyers Gone Bad.First and foremost, Slayton's book is not fiction.The stories of the twenty or so dishonest and unethical lawyers that dominate fourteen of the sixteen chapters of this book are factual.And perhaps it was not Slayton's book itself that generated so much furor, but rather the exclusive interview he gave to Maclean ' magazine following the book's release.4 The magazine catapulted this interview to its cover, proclaiming "Lawyers are rats" and using provocative language and images to portray corruption in the legal profession.Maclean ' shrewdly touted Slayton, a former law professor and Bay Street lawyer, as an insider well placed to write an expos6 of the legal profession.Such covers sell magazines, if not books.The Da Vinci Code challenged fundamental Christian beliefs and was criticized by various established Christian communities, notably the Catholic Church.The Maclean s interview challenged fundamental values of the legal profession-integrity, honesty, and self-governance-and was accordingly
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".