Front Matter for In Search of the Ethical Lawyer: Stories from the Canadian Legal Profession
Bibliographic record
Abstract
This paper contains the front matter - Table of Contents, Foreword by Paul Wells of Maclean's and the Introduction - to this book. We wrote this book in order to attempt to tell the stories about some important cases and people in Canadian legal ethics. All too often appellate cases and texts strip out the facts from cases. However, as any good lawyer will attest, the facts matter. Our goal in writing this book was to put the people back into the conversation about law, at least when it comes to Canadian legal ethics.In Search of the Ethical Lawyer contains stories about important cases, issues and people in Canadian legal ethics. It includes chapters by David Asper, Constance Backhouse, Janine Benedet, Brent Cotter, Richard Devlin, Adam Dodek, Trevor Farrow, Allan Hutchinson, Micah Rankin, Lorne Sossin and Alice Woolley.Here is the Table of Contents: Foreword / Paul WellsIntroduction / Adam Dodek and Alice Woolley1 Keeping Secrets or Saving Lives: What Is a Lawyer to Do? / Adam Dodek2 Putting Up a Defence: Sex, Murder, and Videotapes / Allan C. Hutchinson3 No One’s Interested in Something You Didn’t Do: Freeing David Milgaard the Ugly Way / David Asper4 Begun in Faith, Continued in Determination: Burnley Allan (Rocky) Jones and the Egalitarian Practice of Law / Richard F. Devlin5 Feminist Lawyering: Insiders and Outsiders / Janine Benedet6 Gender and Race in the Construction of Professionalism: Historical Perspectives / Constance Backhouse7 The Helping Profession: Can Pro Bono Lawyers Make Sick Children Well? / Lorne Sossin8 A New Wave of Access to Justice Reform in Canada / Trevor C.W. Farrow9 Michelle’s Story: Creativity and Meaning in Legal Practice / Alice Woolley10 Ian Scott: Renaissance Man, Consummate Advocate, Attorney General Extraordinaire / W. Brent Cotter11 Gerry Laarakker: From Rustic Rambo to Rebel with a Cause / Micah Rankin
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.071 | 0.025 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".