Lawyering with Heart: A Warrior Ethos for Modern Lawyers Reviewing Allan C. Hutchinson, Fighting Fair: Legal Ethics for an Adversarial Age
Bibliographic record
Abstract
Prolific legal theorist Allan C. Hutchinson offers a provocative critical perspective on the relationship between law, the public interest, and lawyers’ practices. His recent book, Fighting Fair, seeks to ground legal ethics in the principles regulating one of the most universal and characteristic of all human activities—warfare. Readers of Candide, All Quiet on the Western Front, or Catch-22, or viewers of Gallipoli, Apocalypse Now, or Hacksaw Ridge may be excused for thinking that all we have learned about war is that it is senseless, brutal, dehumanizing, and in all ways an unmitigated ethical catastrophe. Hutchinson, however, is perfectly serious about the comparison. The adversarial system of dispute resolution is not going anywhere in the common-law world. So rather than seek reform of the “sporting theory of justice,” Hutchinson recommends that lawyers understand their professional role as analogous with that of the warrior. Importantly, a warrior is not a “hired gun,” that familiar target of critics of the adversary system. Warriors are not indifferent to the justice of their employer’s cause. They fight fairly, accept the possibility of defeat as the price of fighting with honour, are committed to a greater good over mere victory, and never lose the connection with their humanity, even in the heat of battle. Warriors also respect their enemies, rather than adopting a “consistently bellicose or thoroughly hostile stance” toward adversaries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".