Bibliographic record
Abstract
This book chapter discusses the use of literary material as a means of studying criminal law. The chapter provides an overview on various methods of combining legal and literary materials (law in literature, literature in law, law as literature, legal aesthetics) and offers two case studies (Susan Glaspell's A Jury of Her Peers and Robert Louis Stevenson's The Strange Case of Dr. Jekyll and Mr. Hyde) to show how literature can open up questions both about substantive criminal law doctrines and also about the grounds on which those doctrines are applied. Along the way, the discussion shows how various scholars of criminal law, such as Nicola Lacey and Anne Coughlin, have raised questions that have also provoked the interest of literary scholars such as Dorrit Cohn and Blakey Vermeule.The chapter also serves as a bibliography for scholars seeking further resources that examine criminal law through the lens of literature. These resources include bibliographies of primary texts (such as crime-based fiction, dying confessions circulated at executions, and movies), secondary texts (discussing law and criminal behavior in relation to fiction, drama, and poetry), and web-based resources (such as the Old Bailey Sessions Papers Online). In that spirit, the chapter also discusses some research that is often overlooked in discussions of criminal law and literature – such as Todd Herzog’s research on Weimar-era true-crime narratives that were created from actual case files; Jonathan Eburne’s research on crime in the work of the French surrealists; Lorna Hutson’s research on civic plots of detection in renaissance drama and their relation to the development of evidence law; and Lisa Rodensky’s work on narrative modes in Victorian fiction and their relation to the treatment of mens rea in contemporaneous legal thought.The chapter closes with some brief reflections on the potential for current work in cognitive literary studies to change the way we think about literature's relation to law, and, in particular, the way we impose narrative templates on the events we experience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".