Bibliographic record
Abstract
Despite the considerable body of work aimed at showing that law is a form of narrative, these efforts have not found many adherents for the view that legal briefs and judicial opinions make better bedtime reading than mystery novels or courtroom dramas. This well-attested preference for fictional narrative suggests that the kind of satisfaction it offers is very different from the pleasures to be had from the genres of professional writing that we associate with forensic advocacy and decision-making. In the latter case, narrative serves the purpose of persuasion. Fiction may also seek to persuade, but more fundamentally it seeks to engage readers in the characters and the events, encouraging a kind of immersion in the story that is hardly necessary, and is rarely attainable, in legal writing. Writers who have been successful in both areas are rare, because they have had to master a variety of skills that are often breezily assumed to be complementary or even cognate, but that turn out to have little in common once we look under the overarching label of “narrative” and try to specify them more concretely. Consider, for example, the roles of dialogue, characterization, and perspective (not to mention the orchestration of events so as to pique the reader’s curiosity, rather than simply to make the details readily comprehensible). The usual forms of legal writing offer no opportunity for cultivating these skills, whereas the novelist can hardly do without them. Alafair Burke is among the few writers who have pursued a truly successful literary career while also producing a significant amount of work in the legal arena. In what follows, I consider the place of the criminal justice system in her most recent novel, The Ex. To provide some context for that discussion, I first show how legal mechanisms for investigating and prosecuting crime have figured in British and American literature over the last three hundred years. Then, I turn to Burke's novel, showing how it uses techniques of literary narrative to conspire with its treatment of doctrinal questions in criminal procedure.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".