Book Note: Creating Legal Worlds: Story And Style In A Culture Of Argument, by Greig Henderson
Bibliographic record
Abstract
WHILE THERE HAS BEEN AMPLE SCHOLARSHIP devoted to examining the legal assumptions, reasons, and analyses underlying judicial decision-making, the narrative aspect of judicial decision-making remains an area less often examined. Drawing on the increasingly popular law and literature movement, in Creating Legal Worlds: Story and Style in a Culture of Argument, Greig Henderson presents the provocative argument that narrative is crucial to legal decision-making. Through the exploration of a number of leading cases from Canada, the United States, and the United Kingdom, Henderson seeks to establish that the rhetoric of storytelling carries as much argumentative weight as the formal logic of legal distinctions and classifications. Henderson’s book is divided into seven short chapters, each of which draws on leading cases from Canada, the United States, and the United Kingdom. However, Henderson repeatedly returns to a few key cases that serve as ongoing examples of judgments as narratives. By examining jurisprudence from different countries, Henderson’s book easily maintains relevance across jurisdictional boundaries. Further, given its unique interdisciplinary focus, Creating Legal Worlds has an audience that clearly transcends the legal community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".