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Record W2765623277 · doi:10.60082/2817-5069.3194

Lawyering with Heart: A Warrior Ethos for Modern Lawyers Reviewing Allan C. Hutchinson, Fighting Fair: Legal Ethics for an Adversarial Age

2017· article· en· W2765623277 on OpenAlexvenueno aff
W. Bradley Wendel

Bibliographic record

VenueOsgoode Hall law journal · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsnot available
Fundersnot available
KeywordsAdversarial systemLawEconomic JusticeHumanityVictoryEthosBattleSociologyLegal ethicsOriginalismPolitical scienceHistoryPoliticsConstitutional law

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.015
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.014
Scholarly communication0.0090.009
Open science0.0010.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.237
GPT teacher head0.362
Teacher spread0.124 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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